1. Table of Contents


Description

1.1 Introduction

1.1.1 Study Objectives

1.1.2 Outcome

1.1.3 Predictors

1.2 Methodology

1.2.1 Feature Selection


Locally Weighted Scatterplot Smoothing Pseudo-R-Squared computes the R-squared statistic - a goodness-of-fit measure which represents explained variability, improvement from null to fitted model and square of the correlation on predicted values obtained from a locally weighted scatterplot smoothing process. LOWESS consists of computing a series of local linear regressions, with each local regression restricted to a window of x-values. Smoothness is achieved by using overlapping windows and by gradually down-weighting points in each regression according to their distance from the anchor point of the window (tri-cube weighting).

Pearson’s Correlation Coefficient is a parametric measure of the linear correlation for a pair of features by calculating the ratio between their covariance and the product of their standard deviations. The presence of high absolute correlation values indicate the univariate association between the numeric predictors and the numeric response.

Spearman’s Rank Correlation Coefficient is a non-parametric measure of the linear correlation for a pair of features by applying the Spearman’s rank equation to the sum of the squared differences between their ranks. The presence of high absolute correlation values indicate the univariate association between the numeric predictors and the numeric response.

Maximal Information Coefficient is an information theory-based measure of two-variable dependence through the computation of the mutual information normalized by the minimum joint entropy. It evaluates the strength of linear or non-linear association using binning as a means to apply mutual information between continuous random variables and selecting the maximum over many possible grids. The presence of high coefficient values indicate the univariate association between the numeric predictors and the numeric response.

Relief Values are heuristic measures which estimate the quality of variables according to how well their values compare to instances that are near to each other, but are efficient in detecting contextual information even with strong dependencies between attributes. Random instances and the corresponding K-nearest instances are selected, with the the weights for the different prediction values, different attributes and different prediction consolidated. The rank of the instance in a sequence of instances ordered by the distance is taken into account based on a a user-defined parameter controlling the influence of the distance. The contributions of each K-nearest instances are normalized by dividing the results with the sum of all K contributions. The presence of high relief values indicate the univariate association between the numeric predictors and the numeric response.

1.2.2 Model Formulation


Stochastic Gradient Boosting is an ensemble learning method which combines multiple weak learners in an additive manner to improve prediction. The process is initialized using a decision tree base learner with the aim of minimizing a specified loss function. The negative gradient of the loss function with respect to the predicted values from the current ensemble is calculated. Residuals are determined as the difference between the actual target values and the learner predictions. A new base learner is subsequently formulated but is trained to predict the residuals. The algorithm involves iteratively improving the ensemble by focusing on the residuals of the previous predictions. Each subsequent base learner is trained to reduce the errors made by the previous ensemble, gradually refining the model’s predictive capabilities.

Random Forest is an ensemble learning method made up of a large set of small decision trees called estimators, with each producing its own prediction. The random forest model aggregates the predictions of the estimators to produce a more accurate prediction. The algorithm involves bootstrap aggregating (where smaller subsets of the training data are repeatedly subsampled with replacement), random subspacing (where a subset of features are sampled and used to train each individual estimator), estimator training (where unpruned decision trees are formulated for each estimator) and inference (where a final prediction is formilated by aggregating the individual predictions of all estimators).

Neural Network comprises of node layers - containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. The process involves the initialization of weights and biases for each neuron in the network. Forward propagation computates the output of the neural network, as determined by the weighted sum of the inputs and a bias term. An activation function is applied to introduce non-linearity to the hidden layer. Back propagation is used to update the weights and the biases in the network by calculating the gradients of the loss function using the chain rule with the magnitude determined by a learning rate. This step allows the network to learn from the errors and adjust the parameters to minimize the loss.

Partial Least Squares applies dimensionality reduction to address high multicollinearity among predictors in a linear regression. The algorithm calculates summary indices termed as partial least squares components which are linear combinations of the original predictors by considering the variation in both the response and the predictor variables. The method of least squares is then applied to fit a linear regression model using the first principal components as predictors, with the optimal number determined using cross-validation.

Cubist Regression is a rule-based model that is an extension of Quinlan’s M5 model tree. A tree is grown where the terminal leaves contain linear regression models. These models are based on the predictors used in previous splits. Also, there are intermediate linear models at each step of the tree. A prediction is made using the linear regression model at the terminal node of the tree, but is smoothed by taking into account the prediction from the linear model in the previous node of the tree (which also occurs recursively up the tree). The tree is reduced to a set of rules, which initially are paths from the top of the tree to the bottom. Rules are eliminated via pruning and/or combined for simplification. The Cubist model can also use a boosting-like scheme called committees where iterative model trees are created in sequence. Another innovation is about using nearest-neighbors to adjust the predictions from the rule-based model.

1.2.3 Model Evaluation


Root Mean Squared Error computes the square root of the average squared difference between the predicted and target values which ranges from zero to infinity. A value of zero indicates perfect prediction of the target values. The metric is weighted according to the square of the error - putting greater influence on large errors than smaller errors which makes it sensitive to outliers but may also encourage conservative prediction.

Median Absolute Deviation computes the median among all absolute difference between the predicted and target values which ranges from zero to infinity. A value of zero indicates perfect prediction of the target values. The metric gives less weight to large errors and is robust to outliers.

R-Squared computes the normalized version of the root mean squared error and also referred to as the coefficient of determination. With a value ranging from zero to infinity, a value of one indicates perfect prediction of the target values. The metric can also be interpreted as the fraction of the total variance in the response variable which can be explained by the model.

1.2.4 Model Post-Hoc Analysis


Variable Importance pertains to model-agnostic methods which allow the comparison of an explanatory variable’s importance between models with different structures. The process involves measuring how much a model’s performance change if the effect of a selected explanatory variable, or of a group of variables, is removed. To remove the effect, perturbations are applied including resampling from an empirical distribution or permutation of the values of the variable. If a variable is important, the model’s performance is expected to worsen after permuting the values of the variable. A larger change in performance indicates the greater importance of the variable.

Partial Dependence Plots show how the expected values of model prediction behave as a function of a selected explanatory variable using the average of a set of individual ceteris paribus profiles. While a ceteris paribus profile shows the dependence of an instance-level prediction on an explanatory variable, a partial dependence profile is estimated by the mean of the ceteris paribus profiles for all instances in a data set.

Breakdown Plots present variable attributions by decomposing the model’s prediction into contributions that can be attributed to the different explanatory variables. Given a prediction which is an approximation of the expected value of the dependent variable driven by the values of explanatory variables, the process involves capturing the contribution of an explanatory variable to the model’s prediction by computing the shift in the expected value of the response, while fixing the values of other variables.

Shapley Additive Explanations are based on Shapley values developed in the cooperative game theory. The process involves explaining a prediction by assuming that each explanatory variable for an instance is a player in a game where the prediction is the payout. The game is the prediction task for a single instance of the data set. The gain is the actual prediction for this instance minus the average prediction for all instances. The players are the explanatory variable values of the instance that collaborate to receive the gain (predict a certain value). The determined value is the average marginal contribution of an explanatory variable across all possible coalitions.

Ceteris Paribus Profiles examine the influence of an explanatory variable by assuming that the values of all other variables do not change. The main objective is to understand how changes in the values of the variable affect the model’s predictions. The process involves evaluating the dependence of the conditional expectation of the response on the values of the particular explanatory variable.

Local Fidelity Plots evaluate the local predictive performance of the model around the observation of interest. The process involves summarizing two distributions of residuals including the residuals for the neighbors of the observation of interest and residuals for the entire training dataset except for neighbors. The results help evaluate whether the model-fit for the instance of interest is unbiased (based on small residuals with distributions symmetric around 0).

Local Stability Plots assess the local stability of predictions around the observation of interest. The process involves checking whether small changes in the explanatory variables, as represented by the changes within the set of neighbors, induce much influence on the predictions. The results help evaluate whether the model is locally additive (based on parallel ceteris paribus profiles) and locally stable (based on adjacent ceteris paribus profiles).

1.3 Results

1.3.1 Data Preparation


[A] The initial tabular dataset was comprised of 394 observations and 23 variables (including 2 metadata, 1 response and 20 predictors).
     [A.1] 394 rows (observations)
     [A.2] 23 columns (variables)
            [A.2.1] 1/23 instance labels = COUNTRY variable (character)
            [A.2.2] 1/23 supplementary information = YEAR variable (numeric)
            [A.2.3] 1/23 response = LIFEXP variable (numeric)
            [A.2.4] 20/23 predictors = 18/20 numeric + 2/20 factor
                     [A.2.4.1] GENDER (factor)
                     [A.2.4.2] CONTIN (factor)
                     [A.2.4.3] UNEMPR (numeric)
                     [A.2.4.4] INFMOR (numeric)
                     [A.2.4.5] GDP (numeric)
                     [A.2.4.6] GNI (numeric)
                     [A.2.4.7] CLTECH (numeric)
                     [A.2.4.8] PERCAP (numeric)
                     [A.2.4.9] RTIMOR (numeric)
                     [A.2.4.10] TUBINC (numeric)
                     [A.2.4.11] DPTIMM (numeric)
                     [A.2.4.12] HEPIMM (numeric)
                     [A.2.4.13] MEAIMM (numeric)
                     [A.2.4.14] HOSBED (numeric)
                     [A.2.4.15] SANSER (numeric)
                     [A.2.4.16] TUBTRT (numeric)
                     [A.2.4.17] URBPOP (numeric)
                     [A.2.4.18] RURPOP (numeric)
                     [A.2.4.19] NCOMOR (numeric)
                     [A.2.4.20] SUIRAT (numeric)

[B] Preliminary transformation was applied to the GDP, GNI and PERCAP variables noted with high ranges.
     [B.1] GDP (numeric)
     [B.2] GNI (numeric)
     [B.3] PERCAP (numeric)

Code Chunk | Output
##################################
# Loading R libraries
##################################
library(DALEX)
library(caret)
library(randomForest)
library(e1071)
library(gbm)
library(skimr)
library(corrplot)
library(lares)
library(dplyr)
library(minerva)
library(CORElearn)
library(patchwork)
library(lime)
library(DALEXtra)

##################################
# Loading source and
# formulating the analysis set
##################################
LED <- read.csv("Life_Expectancy_Data.csv",
                na.strings=c("NA","NaN"," ",""),
                stringsAsFactors = FALSE)
LED <- as.data.frame(LED)

##################################
# Performing a general exploration of the data set
##################################
dim(LED)
## [1] 394  23
str(LED)
## 'data.frame':    394 obs. of  23 variables:
##  $ COUNTRY: chr  "Afghanistan" "Albania" "Algeria" "Angola" ...
##  $ YEAR   : int  2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 ...
##  $ GENDER : chr  "Female" "Female" "Female" "Female" ...
##  $ CONTIN : chr  "Asia" "Europe" "Africa" "Africa" ...
##  $ LIFEXP : num  66.4 80.2 78.1 64 78.1 ...
##  $ UNEMPR : num  14.06 11.32 18.63 7.84 8.26 ...
##  $ INFMOR : num  42.9 7.7 18.6 44.5 5.1 ...
##  $ GDP    : num  1.88e+10 1.54e+10 1.72e+11 8.94e+10 1.69e+09 ...
##  $ GNI    : num  1.91e+10 1.52e+10 1.68e+11 8.19e+10 1.58e+09 ...
##  $ CLTECH : num  36 80.7 99.3 49.6 100 ...
##  $ PERCAP : num  494 5396 3990 2810 17377 ...
##  $ RTIMOR : num  15.9 11.7 20.9 26.1 0 ...
##  $ TUBINC : num  189 16 61 351 0 29 26 2.2 6.9 6 ...
##  $ DPTIMM : num  66 99 91 57 95 ...
##  $ HEPIMM : num  66 99 91 53 99 ...
##  $ MEAIMM : num  64 95 80 51 93 ...
##  $ HOSBED : num  0.432 3.052 1.8 0.8 2.581 ...
##  $ SANSER : num  49 99.2 86.1 51.4 85.5 ...
##  $ TUBTRT : num  91 88 86 69 72.3 ...
##  $ URBPOP : num  25.8 61.2 73.2 66.2 24.5 ...
##  $ RURPOP : num  74.2 38.8 26.8 33.8 75.5 ...
##  $ NCOMOR : num  36.2 6 12.8 19.4 17.6 ...
##  $ SUIRAT : num  3.6 2.7 1.8 2.3 0.8 ...
summary(LED)
##    COUNTRY               YEAR         GENDER             CONTIN         
##  Length:394         Min.   :2019   Length:394         Length:394        
##  Class :character   1st Qu.:2019   Class :character   Class :character  
##  Mode  :character   Median :2019   Mode  :character   Mode  :character  
##                     Mean   :2019                                        
##                     3rd Qu.:2019                                        
##                     Max.   :2019                                        
##      LIFEXP          UNEMPR           INFMOR           GDP           
##  Min.   :51.20   Min.   : 0.071   Min.   : 1.40   Min.   :1.880e+08  
##  1st Qu.:67.61   1st Qu.: 3.560   1st Qu.: 6.00   1st Qu.:1.130e+10  
##  Median :74.33   Median : 5.663   Median :15.20   Median :3.865e+10  
##  Mean   :73.07   Mean   : 7.769   Mean   :21.55   Mean   :4.614e+11  
##  3rd Qu.:79.30   3rd Qu.: 9.842   3rd Qu.:30.66   3rd Qu.:2.450e+11  
##  Max.   :88.10   Max.   :41.153   Max.   :88.80   Max.   :2.140e+13  
##       GNI                CLTECH           PERCAP             RTIMOR    
##  Min.   :3.754e+08   Min.   :  0.00   Min.   :   228.2   Min.   : 0.0  
##  1st Qu.:1.111e+10   1st Qu.: 33.50   1st Qu.:  2229.9   1st Qu.: 8.2  
##  Median :4.005e+10   Median : 79.50   Median :  6609.5   Median :16.0  
##  Mean   :4.814e+11   Mean   : 65.66   Mean   : 16682.2   Mean   :17.0  
##  3rd Qu.:2.450e+11   3rd Qu.:100.00   3rd Qu.: 19303.5   3rd Qu.:23.9  
##  Max.   :2.170e+13   Max.   :100.00   Max.   :175813.9   Max.   :64.6  
##      TUBINC          DPTIMM          HEPIMM          MEAIMM     
##  Min.   :  0.0   Min.   :35.00   Min.   :35.00   Min.   :37.00  
##  1st Qu.: 12.0   1st Qu.:85.69   1st Qu.:81.31   1st Qu.:84.85  
##  Median : 46.0   Median :92.00   Median :91.00   Median :92.00  
##  Mean   :103.5   Mean   :87.87   Mean   :86.64   Mean   :87.21  
##  3rd Qu.:140.0   3rd Qu.:97.00   3rd Qu.:96.00   3rd Qu.:96.00  
##  Max.   :654.0   Max.   :99.00   Max.   :99.00   Max.   :99.00  
##      HOSBED           SANSER            TUBTRT           URBPOP      
##  Min.   : 0.200   Min.   :  8.632   Min.   :  0.00   Min.   : 13.25  
##  1st Qu.: 1.300   1st Qu.: 63.898   1st Qu.: 73.00   1st Qu.: 41.61  
##  Median : 2.570   Median : 91.144   Median : 82.00   Median : 58.76  
##  Mean   : 2.987   Mean   : 77.495   Mean   : 77.68   Mean   : 59.09  
##  3rd Qu.: 3.746   3rd Qu.: 98.516   3rd Qu.: 88.00   3rd Qu.: 77.94  
##  Max.   :13.710   Max.   :100.000   Max.   :100.00   Max.   :100.00  
##      RURPOP          NCOMOR          SUIRAT       
##  Min.   : 0.00   Min.   : 4.40   Min.   :  0.000  
##  1st Qu.:22.06   1st Qu.:13.60   1st Qu.:  3.300  
##  Median :41.24   Median :19.95   Median :  6.850  
##  Mean   :40.91   Mean   :20.02   Mean   :  9.572  
##  3rd Qu.:58.39   3rd Qu.:24.07   3rd Qu.: 11.175  
##  Max.   :86.75   Max.   :58.40   Max.   :116.000
##################################
# Transforming to appropriate data types
##################################
LED$YEAR <- factor(LED$YEAR,
                      levels = c("2019"))
LED$GENDER <- factor(LED$GENDER,
                      levels = c("Male","Female"))
LED$CONTIN <- as.factor(LED$CONTIN)

##################################
# Reducing the range of values
# for certain numeric predictors
##################################
LED$GDP     <- LED$GDP/1000000000
LED$GNI     <- LED$GNI/1000000000
LED$PERCAP  <- LED$PERCAP/1000

##################################
# Formulating a data type assessment summary
##################################
PDA <- LED
(PDA.Summary <- data.frame(
  Column.Index=c(1:length(names(PDA))),
  Column.Name= names(PDA), 
  Column.Type=sapply(PDA, function(x) class(x)), 
  row.names=NULL)
)
##    Column.Index Column.Name Column.Type
## 1             1     COUNTRY   character
## 2             2        YEAR      factor
## 3             3      GENDER      factor
## 4             4      CONTIN      factor
## 5             5      LIFEXP     numeric
## 6             6      UNEMPR     numeric
## 7             7      INFMOR     numeric
## 8             8         GDP     numeric
## 9             9         GNI     numeric
## 10           10      CLTECH     numeric
## 11           11      PERCAP     numeric
## 12           12      RTIMOR     numeric
## 13           13      TUBINC     numeric
## 14           14      DPTIMM     numeric
## 15           15      HEPIMM     numeric
## 16           16      MEAIMM     numeric
## 17           17      HOSBED     numeric
## 18           18      SANSER     numeric
## 19           19      TUBTRT     numeric
## 20           20      URBPOP     numeric
## 21           21      RURPOP     numeric
## 22           22      NCOMOR     numeric
## 23           23      SUIRAT     numeric

1.3.2 Data Quality Assessment


[A] No missing observations noted for any variable.

[B] Low variance observed for 3 numeric predictors with First.Second.Mode.Ratio>5.
     [B.1] SANSER = 12.00
     [B.2] UNEMPR = 11.00
     [B.3] NCOMOR = 6.00

[C] No low variance observed for any predictor with Unique.Count.Ratio<0.01.

[D] High skewness observed for 3 numeric predictors with Skewness>3 or Skewness<(-3)
     [D.1] GDP = 8.62
     [D.2] GNI/span> = 8.55
     [D.3] SUIRAT = 4.08

Code Chunk | Output
##################################
# Loading dataset
##################################
DQA <- LED

##################################
# Formulating an overall data quality assessment summary
##################################
(DQA.Summary <- data.frame(
  Column.Index=c(1:length(names(DQA))),
  Column.Name= names(DQA),
  Column.Type=sapply(DQA, function(x) class(x)),
  Row.Count=sapply(DQA, function(x) nrow(DQA)),
  NA.Count=sapply(DQA,function(x)sum(is.na(x))),
  Fill.Rate=sapply(DQA,function(x)format(round((sum(!is.na(x))/nrow(DQA)),3),nsmall=3)),
  row.names=NULL)
)
##    Column.Index Column.Name Column.Type Row.Count NA.Count Fill.Rate
## 1             1     COUNTRY   character       394        0     1.000
## 2             2        YEAR      factor       394        0     1.000
## 3             3      GENDER      factor       394        0     1.000
## 4             4      CONTIN      factor       394        0     1.000
## 5             5      LIFEXP     numeric       394        0     1.000
## 6             6      UNEMPR     numeric       394        0     1.000
## 7             7      INFMOR     numeric       394        0     1.000
## 8             8         GDP     numeric       394        0     1.000
## 9             9         GNI     numeric       394        0     1.000
## 10           10      CLTECH     numeric       394        0     1.000
## 11           11      PERCAP     numeric       394        0     1.000
## 12           12      RTIMOR     numeric       394        0     1.000
## 13           13      TUBINC     numeric       394        0     1.000
## 14           14      DPTIMM     numeric       394        0     1.000
## 15           15      HEPIMM     numeric       394        0     1.000
## 16           16      MEAIMM     numeric       394        0     1.000
## 17           17      HOSBED     numeric       394        0     1.000
## 18           18      SANSER     numeric       394        0     1.000
## 19           19      TUBTRT     numeric       394        0     1.000
## 20           20      URBPOP     numeric       394        0     1.000
## 21           21      RURPOP     numeric       394        0     1.000
## 22           22      NCOMOR     numeric       394        0     1.000
## 23           23      SUIRAT     numeric       394        0     1.000
##################################
# Listing all Predictors
##################################
DQA.Predictors <- DQA[,!names(DQA) %in% c("COUNTRY","YEAR","LIFEXP")]

##################################
# Listing all numeric Predictors
##################################
DQA.Predictors.Numeric <- DQA.Predictors[,sapply(DQA.Predictors, is.numeric), drop = FALSE]

if (length(names(DQA.Predictors.Numeric))>0) {
    print(paste0("There is (are) ",
               (length(names(DQA.Predictors.Numeric))),
               " numeric descriptor variable(s)."))
} else {
  print("There are no numeric descriptor variables.")
}
## [1] "There is (are) 18 numeric descriptor variable(s)."
##################################
# Listing all factor Predictors
##################################
DQA.Predictors.Factor <- DQA.Predictors[,sapply(DQA.Predictors, is.factor), drop = FALSE]

if (length(names(DQA.Predictors.Factor))>0) {
    print(paste0("There is (are) ",
               (length(names(DQA.Predictors.Factor))),
               " factor descriptor variable(s)."))
} else {
  print("There are no factor descriptor variables.")
}
## [1] "There is (are) 2 factor descriptor variable(s)."
##################################
# Formulating a data quality assessment summary for factor Predictors
##################################
if (length(names(DQA.Predictors.Factor))>0) {

  ##################################
  # Formulating a function to determine the first mode
  ##################################
  FirstModes <- function(x) {
    ux <- unique(na.omit(x))
    tab <- tabulate(match(x, ux))
    ux[tab == max(tab)]
  }

  ##################################
  # Formulating a function to determine the second mode
  ##################################
  SecondModes <- function(x) {
    ux <- unique(na.omit(x))
    tab <- tabulate(match(x, ux))
    fm = ux[tab == max(tab)]
    sm = x[!(x %in% fm)]
    usm <- unique(sm)
    tabsm <- tabulate(match(sm, usm))
    ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
           return("x"),
           return(usm[tabsm == max(tabsm)]))
  }

  (DQA.Predictors.Factor.Summary <- data.frame(
  Column.Name= names(DQA.Predictors.Factor),
  Column.Type=sapply(DQA.Predictors.Factor, function(x) class(x)),
  Unique.Count=sapply(DQA.Predictors.Factor, function(x) length(unique(x))),
  First.Mode.Value=sapply(DQA.Predictors.Factor, function(x) as.character(FirstModes(x)[1])),
  Second.Mode.Value=sapply(DQA.Predictors.Factor, function(x) as.character(SecondModes(x)[1])),
  First.Mode.Count=sapply(DQA.Predictors.Factor, function(x) sum(na.omit(x) == FirstModes(x)[1])),
  Second.Mode.Count=sapply(DQA.Predictors.Factor, function(x) sum(na.omit(x) == SecondModes(x)[1])),
  Unique.Count.Ratio=sapply(DQA.Predictors.Factor, function(x) format(round((length(unique(x))/nrow(DQA.Predictors.Factor)),3), nsmall=3)),
  First.Second.Mode.Ratio=sapply(DQA.Predictors.Factor, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
  row.names=NULL)
  )

}
##   Column.Name Column.Type Unique.Count First.Mode.Value Second.Mode.Value
## 1      GENDER      factor            2           Female                 x
## 2      CONTIN      factor            6           Africa              Asia
##   First.Mode.Count Second.Mode.Count Unique.Count.Ratio First.Second.Mode.Ratio
## 1              197                 0              0.005                     Inf
## 2              106               100              0.015                   1.060
##################################
# Formulating a data quality assessment summary for numeric Predictors
##################################
if (length(names(DQA.Predictors.Numeric))>0) {

  ##################################
  # Formulating a function to determine the first mode
  ##################################
  FirstModes <- function(x) {
    ux <- unique(na.omit(x))
    tab <- tabulate(match(x, ux))
    ux[tab == max(tab)]
  }

  ##################################
  # Formulating a function to determine the second mode
  ##################################
  SecondModes <- function(x) {
    ux <- unique(na.omit(x))
    tab <- tabulate(match(x, ux))
    fm = ux[tab == max(tab)]
    sm = na.omit(x)[!(na.omit(x) %in% fm)]
    usm <- unique(sm)
    tabsm <- tabulate(match(sm, usm))
    ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
           return(0.00001),
           return(usm[tabsm == max(tabsm)]))
  }

  (DQA.Predictors.Numeric.Summary <- data.frame(
  Column.Name= names(DQA.Predictors.Numeric),
  Column.Type=sapply(DQA.Predictors.Numeric, function(x) class(x)),
  Unique.Count=sapply(DQA.Predictors.Numeric, function(x) length(unique(x))),
  Unique.Count.Ratio=sapply(DQA.Predictors.Numeric, function(x) format(round((length(unique(x))/nrow(DQA.Predictors.Numeric)),3), nsmall=3)),
  First.Mode.Value=sapply(DQA.Predictors.Numeric, function(x) format(round((FirstModes(x)[1]),3),nsmall=3)),
  Second.Mode.Value=sapply(DQA.Predictors.Numeric, function(x) format(round((SecondModes(x)[1]),3),nsmall=3)),
  First.Mode.Count=sapply(DQA.Predictors.Numeric, function(x) sum(na.omit(x) == FirstModes(x)[1])),
  Second.Mode.Count=sapply(DQA.Predictors.Numeric, function(x) sum(na.omit(x) == SecondModes(x)[1])),
  First.Second.Mode.Ratio=sapply(DQA.Predictors.Numeric, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
  Minimum=sapply(DQA.Predictors.Numeric, function(x) format(round(min(x,na.rm = TRUE),3), nsmall=3)),
  Mean=sapply(DQA.Predictors.Numeric, function(x) format(round(mean(x,na.rm = TRUE),3), nsmall=3)),
  Median=sapply(DQA.Predictors.Numeric, function(x) format(round(median(x,na.rm = TRUE),3), nsmall=3)),
  Maximum=sapply(DQA.Predictors.Numeric, function(x) format(round(max(x,na.rm = TRUE),3), nsmall=3)),
  Skewness=sapply(DQA.Predictors.Numeric, function(x) format(round(skewness(x,na.rm = TRUE),3), nsmall=3)),
  Kurtosis=sapply(DQA.Predictors.Numeric, function(x) format(round(kurtosis(x,na.rm = TRUE),3), nsmall=3)),
  Percentile25th=sapply(DQA.Predictors.Numeric, function(x) format(round(quantile(x,probs=0.25,na.rm = TRUE),3), nsmall=3)),
  Percentile75th=sapply(DQA.Predictors.Numeric, function(x) format(round(quantile(x,probs=0.75,na.rm = TRUE),3), nsmall=3)),
  row.names=NULL)
  )

}
##    Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 1       UNEMPR     numeric          370              0.939            8.256
## 2       INFMOR     numeric          245              0.622           30.235
## 3          GDP     numeric          254              0.645          303.000
## 4          GNI     numeric          253              0.642         2040.000
## 5       CLTECH     numeric          112              0.284          100.000
## 6       PERCAP     numeric          196              0.497           12.669
## 7       RTIMOR     numeric          141              0.358           18.229
## 8       TUBINC     numeric          146              0.371          136.043
## 9       DPTIMM     numeric           45              0.114           99.000
## 10      HEPIMM     numeric           45              0.114           81.308
## 11      MEAIMM     numeric           48              0.122           99.000
## 12      HOSBED     numeric          173              0.439            2.986
## 13      SANSER     numeric          186              0.472          100.000
## 14      TUBTRT     numeric           59              0.150           84.000
## 15      URBPOP     numeric          191              0.485          100.000
## 16      RURPOP     numeric          191              0.485            0.000
## 17      NCOMOR     numeric          214              0.543           22.100
## 18      SUIRAT     numeric          176              0.447           10.619
##    Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 1              3.924               22                 2                  11.000
## 2              2.100               28                 7                   4.000
## 3            279.000                4                 3                   1.333
## 4            316.000                8                 4                   2.000
## 5             60.593              108                34                   3.176
## 6              0.494                4                 2                   2.000
## 7             26.800               28                 6                   4.667
## 8             35.000               12                10                   1.200
## 9             85.685               44                30                   1.467
## 10            99.000               40                38                   1.053
## 11            84.855               48                30                   1.600
## 12             0.400               34                 8                   4.250
## 13            49.006               24                 2                  12.000
## 14            83.000               22                20                   1.100
## 15            55.985               10                 4                   2.500
## 16            44.015               10                 4                   2.500
## 17             6.800               30                 5                   6.000
## 18             7.600               30                 8                   3.750
##    Minimum    Mean Median   Maximum Skewness Kurtosis Percentile25th
## 1    0.071   7.769  5.663    41.153    1.751    3.680          3.560
## 2    1.400  21.546 15.200    88.800    1.084    0.567          6.000
## 3    0.188 461.374 38.653 21400.000    8.619   82.715         11.304
## 4    0.375 481.441 40.048 21700.000    8.547   82.139         11.110
## 5    0.000  65.660 79.500   100.000   -0.623   -1.141         33.500
## 6    0.228  16.682  6.610   175.814    2.810   10.880          2.230
## 7    0.000  17.003 16.000    64.600    0.740    1.028          8.200
## 8    0.000 103.489 46.000   654.000    1.864    3.177         12.000
## 9   35.000  87.875 92.000    99.000   -1.856    3.434         85.685
## 10  35.000  86.640 91.000    99.000   -1.595    2.477         81.308
## 11  37.000  87.207 92.000    99.000   -1.688    2.574         84.855
## 12   0.200   2.987  2.570    13.710    1.697    3.859          1.300
## 13   8.632  77.495 91.144   100.000   -1.122   -0.155         63.898
## 14   0.000  77.675 82.000   100.000   -2.194    5.571         73.000
## 15  13.250  59.094 58.760   100.000   -0.132   -0.991         41.612
## 16   0.000  40.906 41.240    86.750    0.132   -0.991         22.058
## 17   4.400  20.021 19.950    58.400    0.864    1.551         13.600
## 18   0.000   9.572  6.850   116.000    4.082   29.352          3.300
##    Percentile75th
## 1           9.842
## 2          30.659
## 3         245.000
## 4         245.000
## 5         100.000
## 6          19.304
## 7          23.900
## 8         140.000
## 9          97.000
## 10         96.000
## 11         96.000
## 12          3.746
## 13         98.516
## 14         88.000
## 15         77.942
## 16         58.388
## 17         24.075
## 18         11.175
##################################
# Identifying potential data quality issues
##################################

##################################
# Checking for missing observations
##################################
if ((nrow(DQA.Summary[DQA.Summary$NA.Count>0,]))>0){
  print(paste0("Missing observations noted for ",
               (nrow(DQA.Summary[DQA.Summary$NA.Count>0,])),
               " variable(s) with NA.Count>0 and Fill.Rate<1.0."))
  DQA.Summary[DQA.Summary$NA.Count>0,]
} else {
  print("No missing observations noted.")
}
## [1] "No missing observations noted."
##################################
# Checking for zero or near-zero variance Predictors
##################################
if (length(names(DQA.Predictors.Factor))==0) {
  print("No factor Predictors noted.")
} else if (nrow(DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,])>0){
  print(paste0("Low variance observed for ",
               (nrow(DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,])),
               " factor variable(s) with First.Second.Mode.Ratio>5."))
  DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,]
} else {
  print("No low variance factor Predictors due to high first-second mode ratio noted.")
}
## [1] "Low variance observed for 1 factor variable(s) with First.Second.Mode.Ratio>5."
##   Column.Name Column.Type Unique.Count First.Mode.Value Second.Mode.Value
## 1      GENDER      factor            2           Female                 x
##   First.Mode.Count Second.Mode.Count Unique.Count.Ratio First.Second.Mode.Ratio
## 1              197                 0              0.005                     Inf
if (length(names(DQA.Predictors.Numeric))==0) {
  print("No numeric Predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,])>0){
  print(paste0("Low variance observed for ",
               (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,])),
               " numeric variable(s) with First.Second.Mode.Ratio>5."))
  DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,]
} else {
  print("No low variance numeric Predictors due to high first-second mode ratio noted.")
}
## [1] "Low variance observed for 3 numeric variable(s) with First.Second.Mode.Ratio>5."
##    Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 1       UNEMPR     numeric          370              0.939            8.256
## 13      SANSER     numeric          186              0.472          100.000
## 17      NCOMOR     numeric          214              0.543           22.100
##    Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 1              3.924               22                 2                  11.000
## 13            49.006               24                 2                  12.000
## 17             6.800               30                 5                   6.000
##    Minimum   Mean Median Maximum Skewness Kurtosis Percentile25th
## 1    0.071  7.769  5.663  41.153    1.751    3.680          3.560
## 13   8.632 77.495 91.144 100.000   -1.122   -0.155         63.898
## 17   4.400 20.021 19.950  58.400    0.864    1.551         13.600
##    Percentile75th
## 1           9.842
## 13         98.516
## 17         24.075
if (length(names(DQA.Predictors.Numeric))==0) {
  print("No numeric Predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,])>0){
  print(paste0("Low variance observed for ",
               (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,])),
               " numeric variable(s) with Unique.Count.Ratio<0.01."))
  DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,]
} else {
  print("No low variance numeric Predictors due to low unique count ratio noted.")
}
## [1] "No low variance numeric Predictors due to low unique count ratio noted."
##################################
# Checking for skewed Predictors
##################################
if (length(names(DQA.Predictors.Numeric))==0) {
  print("No numeric Predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
                                               as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),])>0){
  print(paste0("High skewness observed for ",
  (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
                                               as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),])),
  " numeric variable(s) with Skewness>3 or Skewness<(-3)."))
  DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
                                 as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),]
} else {
  print("No skewed numeric Predictors noted.")
}
## [1] "High skewness observed for 3 numeric variable(s) with Skewness>3 or Skewness<(-3)."
##    Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 3          GDP     numeric          254              0.645          303.000
## 4          GNI     numeric          253              0.642         2040.000
## 18      SUIRAT     numeric          176              0.447           10.619
##    Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 3            279.000                4                 3                   1.333
## 4            316.000                8                 4                   2.000
## 18             7.600               30                 8                   3.750
##    Minimum    Mean Median   Maximum Skewness Kurtosis Percentile25th
## 3    0.188 461.374 38.653 21400.000    8.619   82.715         11.304
## 4    0.375 481.441 40.048 21700.000    8.547   82.139         11.110
## 18   0.000   9.572  6.850   116.000    4.082   29.352          3.300
##    Percentile75th
## 3         245.000
## 4         245.000
## 18         11.175

1.3.3 Data Preprocessing


1.3.3.1 Outlier Treatment


Code Chunk | Output
##################################
# Loading dataset
##################################
DPA <- LED

##################################
# Gathering descriptive statistics
##################################
(DPA_Skimmed <- skim(DPA))
Data summary
Name DPA
Number of rows 394
Number of columns 23
_______________________
Column type frequency:
character 1
factor 3
numeric 19
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
COUNTRY 0 1 4 30 0 197 0

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
YEAR 0 1 FALSE 1 201: 394
GENDER 0 1 FALSE 2 Mal: 197, Fem: 197
CONTIN 0 1 FALSE 6 Afr: 106, Asi: 100, Eur: 86, Nor: 52

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
LIFEXP 0 1 73.07 7.82 51.20 67.61 74.32 79.30 88.10 ▁▃▆▇▃
UNEMPR 0 1 7.77 6.35 0.07 3.56 5.66 9.84 41.15 ▇▂▁▁▁
INFMOR 0 1 21.55 18.67 1.40 6.00 15.20 30.66 88.80 ▇▃▂▁▁
GDP 0 1 461.37 1920.36 0.19 11.30 38.65 245.00 21400.00 ▇▁▁▁▁
GNI 0 1 481.44 1942.86 0.38 11.11 40.05 245.00 21700.00 ▇▁▁▁▁
CLTECH 0 1 65.66 36.33 0.00 33.50 79.50 100.00 100.00 ▃▁▁▂▇
PERCAP 0 1 16.68 24.32 0.23 2.23 6.61 19.30 175.81 ▇▁▁▁▁
RTIMOR 0 1 17.00 10.34 0.00 8.20 16.00 23.90 64.60 ▇▇▅▁▁
TUBINC 0 1 103.49 133.68 0.00 12.00 46.00 140.00 654.00 ▇▂▁▁▁
DPTIMM 0 1 87.87 12.41 35.00 85.69 92.00 97.00 99.00 ▁▁▁▃▇
HEPIMM 0 1 86.64 12.72 35.00 81.31 91.00 96.00 99.00 ▁▁▁▃▇
MEAIMM 0 1 87.21 13.17 37.00 84.85 92.00 96.00 99.00 ▁▁▁▃▇
HOSBED 0 1 2.99 2.35 0.20 1.30 2.57 3.75 13.71 ▇▅▂▁▁
SANSER 0 1 77.49 27.63 8.63 63.90 91.14 98.52 100.00 ▁▁▁▂▇
TUBTRT 0 1 77.68 16.97 0.00 73.00 82.00 88.00 100.00 ▁▁▁▅▇
URBPOP 0 1 59.09 23.24 13.25 41.61 58.76 77.94 100.00 ▅▆▇▇▆
RURPOP 0 1 40.91 23.24 0.00 22.06 41.24 58.39 86.75 ▆▇▇▆▅
NCOMOR 0 1 20.02 8.40 4.40 13.60 19.95 24.08 58.40 ▅▇▂▁▁
SUIRAT 0 1 9.57 10.49 0.00 3.30 6.85 11.17 116.00 ▇▁▁▁▁
##################################
# Outlier Treatment
##################################

##################################
# Listing all Predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("COUNTRY","YEAR","LIFEXP")]

##################################
# Listing all numeric predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]

##################################
# Identifying outliers for the numeric predictors
##################################
OutlierCountList <- c()

for (i in 1:ncol(DPA.Predictors.Numeric)) {
  Outliers <- boxplot.stats(DPA.Predictors.Numeric[,i])$out
  OutlierCount <- length(Outliers)
  OutlierCountList <- append(OutlierCountList,OutlierCount)
  OutlierIndices <- which(DPA.Predictors.Numeric[,i] %in% c(Outliers))
  print(
  ggplot(DPA.Predictors.Numeric, aes(x=DPA.Predictors.Numeric[,i])) +
  geom_boxplot() +
  theme_bw() +
  theme(axis.text.y=element_blank(), 
        axis.ticks.y=element_blank()) +
  xlab(names(DPA.Predictors.Numeric)[i]) +
  labs(title=names(DPA.Predictors.Numeric)[i],
       subtitle=paste0(OutlierCount, " Outlier(s) Detected")))
}

##################################
# Formulating the histogram
# for the numeric predictors
##################################

for (i in 1:ncol(DPA.Predictors.Numeric)) {
  Median <- format(round(median(DPA.Predictors.Numeric[,i],na.rm = TRUE),2), nsmall=2)
  Mean <- format(round(mean(DPA.Predictors.Numeric[,i],na.rm = TRUE),2), nsmall=2)
  Skewness <- format(round(skewness(DPA.Predictors.Numeric[,i],na.rm = TRUE),2), nsmall=2)
  print(
  ggplot(DPA.Predictors.Numeric, aes(x=DPA.Predictors.Numeric[,i])) +
  geom_histogram(binwidth=1,color="black", fill="white") +
  geom_vline(aes(xintercept=mean(DPA.Predictors.Numeric[,i])),
            color="blue", size=1) +
    geom_vline(aes(xintercept=median(DPA.Predictors.Numeric[,i])),
            color="red", size=1) +
  theme_bw() +
  ylab("Count") +
  xlab(names(DPA.Predictors.Numeric)[i]) +
  labs(title=names(DPA.Predictors.Numeric)[i],
       subtitle=paste0("Median = ", Median,
                       ", Mean = ", Mean,
                       ", Skewness = ", Skewness)))
}

##################################
# Investigating distributional anomalies
# observed for several predictors 
##################################
(INFMOR_Unique <- DPA %>%
  group_by(INFMOR) %>%
  summarize(Distinct_INFMOR = n_distinct(COUNTRY)) %>%
  arrange(desc(Distinct_INFMOR)) %>%
  slice(1:5))
## # A tibble: 5 x 2
##   INFMOR Distinct_INFMOR
##    <dbl>           <int>
## 1   30.2              14
## 2    2.1               7
## 3    6.4               6
## 4    1.7               4
## 5    2.5               4
(INFMOR_Unique_Country <- DPA[round(DPA$INFMOR,digits=1)==30.2,c("COUNTRY")])
##  [1] "Aruba"                    "Bermuda"                 
##  [3] "Channel Islands"          "Faroe Islands"           
##  [5] "French Polynesia"         "Guam"                    
##  [7] "Hong Kong SAR, China"     "Kosovo"                  
##  [9] "Liechtenstein"            "Macao SAR, China"        
## [11] "New Caledonia"            "Puerto Rico"             
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"   
## [15] "Aruba"                    "Bermuda"                 
## [17] "Channel Islands"          "Faroe Islands"           
## [19] "French Polynesia"         "Guam"                    
## [21] "Hong Kong SAR, China"     "Kosovo"                  
## [23] "Liechtenstein"            "Macao SAR, China"        
## [25] "New Caledonia"            "Puerto Rico"             
## [27] "St. Martin (French part)" "Virgin Islands (U.S.)"
DPA %>%
  group_by(CLTECH) %>%
  summarize(Distinct_CLTECH = n_distinct(COUNTRY)) %>%
  arrange(desc(Distinct_CLTECH)) %>%
  slice(1:5)
## # A tibble: 5 x 2
##   CLTECH Distinct_CLTECH
##    <dbl>           <int>
## 1 100                 54
## 2  60.6               17
## 3   9.30               3
## 4  99.9                3
## 5   0.2                2
(CLTECH_Unique_Country <- DPA[round(DPA$CLTECH,digits=1)==60.6,c("COUNTRY")])
##  [1] "Aruba"                    "Bermuda"                 
##  [3] "Channel Islands"          "Faroe Islands"           
##  [5] "French Polynesia"         "Guam"                    
##  [7] "Hong Kong SAR, China"     "Kosovo"                  
##  [9] "Lebanon"                  "Libya"                   
## [11] "Liechtenstein"            "Macao SAR, China"        
## [13] "New Caledonia"            "Puerto Rico"             
## [15] "St. Martin (French part)" "Virgin Islands (U.S.)"   
## [17] "West Bank and Gaza"       "Aruba"                   
## [19] "Bermuda"                  "Channel Islands"         
## [21] "Faroe Islands"            "French Polynesia"        
## [23] "Guam"                     "Hong Kong SAR, China"    
## [25] "Kosovo"                   "Lebanon"                 
## [27] "Libya"                    "Liechtenstein"           
## [29] "Macao SAR, China"         "New Caledonia"           
## [31] "Puerto Rico"              "St. Martin (French part)"
## [33] "Virgin Islands (U.S.)"    "West Bank and Gaza"
DPA %>%
  group_by(RTIMOR) %>%
  summarize(Distinct_RTIMOR = n_distinct(COUNTRY)) %>%
  arrange(desc(Distinct_RTIMOR)) %>%
  slice(1:5)
## # A tibble: 5 x 2
##   RTIMOR Distinct_RTIMOR
##    <dbl>           <int>
## 1   18.2              14
## 2    3.9               3
## 3    5.1               3
## 4    5.3               3
## 5   12.7               3
(RTIMOR_Unique_Country <- DPA[round(DPA$RTIMOR,digits=1)==18.2,c("COUNTRY")])
##  [1] "Aruba"                    "Bermuda"                 
##  [3] "Channel Islands"          "Faroe Islands"           
##  [5] "French Polynesia"         "Guam"                    
##  [7] "Hong Kong SAR, China"     "Kosovo"                  
##  [9] "Liechtenstein"            "Macao SAR, China"        
## [11] "New Caledonia"            "Puerto Rico"             
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"   
## [15] "Aruba"                    "Bermuda"                 
## [17] "Channel Islands"          "Faroe Islands"           
## [19] "French Polynesia"         "Guam"                    
## [21] "Hong Kong SAR, China"     "Kosovo"                  
## [23] "Liechtenstein"            "Macao SAR, China"        
## [25] "New Caledonia"            "Puerto Rico"             
## [27] "St. Martin (French part)" "Virgin Islands (U.S.)"
DPA %>%
  group_by(DPTIMM) %>%
  summarize(Distinct_DPTIMM = n_distinct(COUNTRY)) %>%
  arrange(desc(Distinct_DPTIMM)) %>%
  slice(1:5)
## # A tibble: 5 x 2
##   DPTIMM Distinct_DPTIMM
##    <dbl>           <int>
## 1   99                22
## 2   85.7              15
## 3   97                14
## 4   98                14
## 5   95                13
(DPTIMM_Unique_Country <- DPA[round(DPA$DPTIMM,digits=1)==85.7,c("COUNTRY")])
##  [1] "Aruba"                    "Bermuda"                 
##  [3] "Channel Islands"          "Faroe Islands"           
##  [5] "French Polynesia"         "Guam"                    
##  [7] "Hong Kong SAR, China"     "Kosovo"                  
##  [9] "Liechtenstein"            "Macao SAR, China"        
## [11] "New Caledonia"            "Puerto Rico"             
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"   
## [15] "West Bank and Gaza"       "Aruba"                   
## [17] "Bermuda"                  "Channel Islands"         
## [19] "Faroe Islands"            "French Polynesia"        
## [21] "Guam"                     "Hong Kong SAR, China"    
## [23] "Kosovo"                   "Liechtenstein"           
## [25] "Macao SAR, China"         "New Caledonia"           
## [27] "Puerto Rico"              "St. Martin (French part)"
## [29] "Virgin Islands (U.S.)"    "West Bank and Gaza"
DPA %>%
  group_by(HEPIMM) %>%
  summarize(Distinct_HEPIMM = n_distinct(COUNTRY)) %>%
  arrange(desc(Distinct_HEPIMM)) %>%
  slice(1:5)
## # A tibble: 5 x 2
##   HEPIMM Distinct_HEPIMM
##    <dbl>           <int>
## 1   81.3              20
## 2   99                19
## 3   97                17
## 4   98                11
## 5   92                10
(HEPIMM_Unique_Country <- DPA[round(DPA$HEPIMM,digits=1)==81.3,c("COUNTRY")])
##  [1] "Aruba"                    "Bermuda"                 
##  [3] "Channel Islands"          "Denmark"                 
##  [5] "Faroe Islands"            "Finland"                 
##  [7] "French Polynesia"         "Guam"                    
##  [9] "Hong Kong SAR, China"     "Hungary"                 
## [11] "Iceland"                  "Kosovo"                  
## [13] "Liechtenstein"            "Macao SAR, China"        
## [15] "New Caledonia"            "Puerto Rico"             
## [17] "Slovenia"                 "St. Martin (French part)"
## [19] "Virgin Islands (U.S.)"    "West Bank and Gaza"      
## [21] "Aruba"                    "Bermuda"                 
## [23] "Channel Islands"          "Denmark"                 
## [25] "Faroe Islands"            "Finland"                 
## [27] "French Polynesia"         "Guam"                    
## [29] "Hong Kong SAR, China"     "Hungary"                 
## [31] "Iceland"                  "Kosovo"                  
## [33] "Liechtenstein"            "Macao SAR, China"        
## [35] "New Caledonia"            "Puerto Rico"             
## [37] "Slovenia"                 "St. Martin (French part)"
## [39] "Virgin Islands (U.S.)"    "West Bank and Gaza"
DPA %>%
  group_by(MEAIMM) %>%
  summarize(Distinct_MEAIMM = n_distinct(COUNTRY)) %>%
  arrange(desc(Distinct_MEAIMM)) %>%
  slice(1:5)
## # A tibble: 5 x 2
##   MEAIMM Distinct_MEAIMM
##    <dbl>           <int>
## 1   99                24
## 2   84.9              15
## 3   95                14
## 4   96                14
## 5   98                13
(MEAIMM_Unique_Country <- DPA[round(DPA$MEAIMM,digits=1)==84.9,c("COUNTRY")])
##  [1] "Aruba"                    "Bermuda"                 
##  [3] "Channel Islands"          "Faroe Islands"           
##  [5] "French Polynesia"         "Guam"                    
##  [7] "Hong Kong SAR, China"     "Kosovo"                  
##  [9] "Liechtenstein"            "Macao SAR, China"        
## [11] "New Caledonia"            "Puerto Rico"             
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"   
## [15] "West Bank and Gaza"       "Aruba"                   
## [17] "Bermuda"                  "Channel Islands"         
## [19] "Faroe Islands"            "French Polynesia"        
## [21] "Guam"                     "Hong Kong SAR, China"    
## [23] "Kosovo"                   "Liechtenstein"           
## [25] "Macao SAR, China"         "New Caledonia"           
## [27] "Puerto Rico"              "St. Martin (French part)"
## [29] "Virgin Islands (U.S.)"    "West Bank and Gaza"
DPA %>%
  group_by(HOSBED) %>%
  summarize(Distinct_HOSBED = n_distinct(COUNTRY)) %>%
  arrange(desc(Distinct_HOSBED)) %>%
  slice(1:5)
## # A tibble: 5 x 2
##   HOSBED Distinct_HOSBED
##    <dbl>           <int>
## 1   2.99              17
## 2   0.4                4
## 3   0.8                2
## 4   0.85               2
## 5   0.9                2
(HOSBED_Unique_Country <- DPA[round(DPA$HOSBED,digits=1)==3.0,c("COUNTRY")])
##  [1] "Aruba"                    "Bermuda"                 
##  [3] "Channel Islands"          "Faroe Islands"           
##  [5] "French Polynesia"         "Guam"                    
##  [7] "Hong Kong SAR, China"     "Kosovo"                  
##  [9] "Liechtenstein"            "Macao SAR, China"        
## [11] "Namibia"                  "New Caledonia"           
## [13] "Papua New Guinea"         "Puerto Rico"             
## [15] "South Sudan"              "St. Martin (French part)"
## [17] "Virgin Islands (U.S.)"    "West Bank and Gaza"      
## [19] "Aruba"                    "Bermuda"                 
## [21] "Channel Islands"          "Faroe Islands"           
## [23] "French Polynesia"         "Guam"                    
## [25] "Hong Kong SAR, China"     "Kosovo"                  
## [27] "Liechtenstein"            "Macao SAR, China"        
## [29] "Namibia"                  "New Caledonia"           
## [31] "Papua New Guinea"         "Puerto Rico"             
## [33] "South Sudan"              "St. Martin (French part)"
## [35] "Virgin Islands (U.S.)"    "West Bank and Gaza"
DPA %>%
  group_by(NCOMOR) %>%
  summarize(Distinct_NCOMOR = n_distinct(COUNTRY)) %>%
  arrange(desc(Distinct_NCOMOR)) %>%
  slice(1:5)
## # A tibble: 5 x 2
##   NCOMOR Distinct_NCOMOR
##    <dbl>           <int>
## 1   22.1              15
## 2    6.8               5
## 3   13.6               5
## 4   15.2               5
## 5   17.5               5
(NCOMOR_Unique_Country <- DPA[round(DPA$NCOMOR,digits=1)==22.1,c("COUNTRY")])
##  [1] "Aruba"                    "Bermuda"                 
##  [3] "Burkina Faso"             "Channel Islands"         
##  [5] "Faroe Islands"            "French Polynesia"        
##  [7] "Guam"                     "Hong Kong SAR, China"    
##  [9] "Kosovo"                   "Liechtenstein"           
## [11] "Macao SAR, China"         "New Caledonia"           
## [13] "Puerto Rico"              "St. Martin (French part)"
## [15] "Virgin Islands (U.S.)"    "West Bank and Gaza"      
## [17] "Aruba"                    "Bermuda"                 
## [19] "Channel Islands"          "Dominican Republic"      
## [21] "Equatorial Guinea"        "Estonia"                 
## [23] "Faroe Islands"            "French Polynesia"        
## [25] "Guam"                     "Hong Kong SAR, China"    
## [27] "Kosovo"                   "Liechtenstein"           
## [29] "Macao SAR, China"         "New Caledonia"           
## [31] "Puerto Rico"              "Sierra Leone"            
## [33] "St. Martin (French part)" "Virgin Islands (U.S.)"   
## [35] "West Bank and Gaza"
DPA %>%
  group_by(SUIRAT) %>%
  summarize(Distinct_SUIRAT = n_distinct(COUNTRY)) %>%
  arrange(desc(Distinct_SUIRAT)) %>%
  slice(1:5)
## # A tibble: 5 x 2
##   SUIRAT Distinct_SUIRAT
##    <dbl>           <int>
## 1   10.6              15
## 2    7.6               8
## 3    1.7               7
## 4    2                 7
## 5    2.8               7
(SUIRAT_Unique_Country <- DPA[round(DPA$SUIRAT,digits=1)==10.6,c("COUNTRY")])
##  [1] "Aruba"                    "Bermuda"                 
##  [3] "Channel Islands"          "Faroe Islands"           
##  [5] "French Polynesia"         "Guam"                    
##  [7] "Hong Kong SAR, China"     "Kosovo"                  
##  [9] "Liechtenstein"            "Macao SAR, China"        
## [11] "New Caledonia"            "Puerto Rico"             
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"   
## [15] "West Bank and Gaza"       "Aruba"                   
## [17] "Bermuda"                  "Channel Islands"         
## [19] "Congo, Dem. Rep."         "Faroe Islands"           
## [21] "French Polynesia"         "Guam"                    
## [23] "Hong Kong SAR, China"     "Kosovo"                  
## [25] "Liechtenstein"            "Macao SAR, China"        
## [27] "New Caledonia"            "Puerto Rico"             
## [29] "St. Martin (French part)" "Virgin Islands (U.S.)"   
## [31] "West Bank and Gaza"
(AnomalousVariables_Unique_Country <- MEAIMM_Unique_Country)
##  [1] "Aruba"                    "Bermuda"                 
##  [3] "Channel Islands"          "Faroe Islands"           
##  [5] "French Polynesia"         "Guam"                    
##  [7] "Hong Kong SAR, China"     "Kosovo"                  
##  [9] "Liechtenstein"            "Macao SAR, China"        
## [11] "New Caledonia"            "Puerto Rico"             
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"   
## [15] "West Bank and Gaza"       "Aruba"                   
## [17] "Bermuda"                  "Channel Islands"         
## [19] "Faroe Islands"            "French Polynesia"        
## [21] "Guam"                     "Hong Kong SAR, China"    
## [23] "Kosovo"                   "Liechtenstein"           
## [25] "Macao SAR, China"         "New Caledonia"           
## [27] "Puerto Rico"              "St. Martin (French part)"
## [29] "Virgin Islands (U.S.)"    "West Bank and Gaza"
##################################
# Removing associated rows associated
# with anomalous variables
##################################
dim(DPA)
## [1] 394  23
DPA <- DPA[!(DPA$COUNTRY %in% AnomalousVariables_Unique_Country),]
dim(DPA)
## [1] 364  23
##################################
# Listing all Predictors
# for the updated data
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("COUNTRY","YEAR","LIFEXP")]

##################################
# Listing all numeric predictors
# for the updated data
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]

1.3.3.2 Zero and Near-Zero Variance


Code Chunk | Output
##################################
# Zero and Near-Zero Variance
##################################

##################################
# Identifying columns with low variance
###################################
DPA_LowVariance <- nearZeroVar(DPA,
                               freqCut = 80/20,
                               uniqueCut = 10,
                               saveMetrics= TRUE)
(DPA_LowVariance[DPA_LowVariance$nzv,])
##      freqRatio percentUnique zeroVar  nzv
## YEAR         0     0.2747253    TRUE TRUE
if ((nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))==0){
  
  print("No low variance predictors noted.")
  
} else {

  print(paste0("Low variance observed for ",
               (nrow(DPA_LowVariance[DPA_LowVariance$nzv,])),
               " numeric variable(s) with First.Second.Mode.Ratio>4 and Unique.Count.Ratio<0.10."))
  
  DPA_LowVarianceForRemoval <- (nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))
  
  print(paste0("Low variance can be resolved by removing ",
               (nrow(DPA_LowVariance[DPA_LowVariance$nzv,])),
               " numeric variable(s)."))
  
  for (j in 1:DPA_LowVarianceForRemoval) {
  DPA_LowVarianceRemovedVariable <- rownames(DPA_LowVariance[DPA_LowVariance$nzv,])[j]
  print(paste0("Variable ",
               j,
               " for removal: ",
               DPA_LowVarianceRemovedVariable))
  }
  
  DPA %>%
  skim() %>%
  dplyr::filter(skim_variable %in% rownames(DPA_LowVariance[DPA_LowVariance$nzv,]))

}
## [1] "Low variance observed for 1 numeric variable(s) with First.Second.Mode.Ratio>4 and Unique.Count.Ratio<0.10."
## [1] "Low variance can be resolved by removing 1 numeric variable(s)."
## [1] "Variable 1 for removal: YEAR"
Data summary
Name Piped data
Number of rows 364
Number of columns 23
_______________________
Column type frequency:
factor 1
________________________
Group variables None

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
YEAR 0 1 FALSE 1 201: 364

1.3.3.3 Collinearity


Code Chunk | Output
##################################
# Collinearity
##################################

##################################
# Visualizing pairwise correlation between predictors
##################################
DPA_CorrelationTest <- cor.mtest(DPA.Predictors.Numeric,
                       method = "pearson",
                       conf.level = .95)

corrplot(cor(DPA.Predictors.Numeric,
             method = "pearson",
             use="pairwise.complete.obs"), 
         method = "circle",
         type = "upper", 
         order = "original", 
         tl.col = "black", 
         tl.cex = 0.75,
         tl.srt = 90, 
         sig.level = 0.05, 
         p.mat = DPA_CorrelationTest$p,
         insig = "blank")

##################################
# Identifying the highly correlated variables
##################################
DPA_Correlation <-  cor(DPA.Predictors.Numeric, 
                        method = "pearson",
                        use="pairwise.complete.obs")
(DPA_HighlyCorrelatedCount <- sum(abs(DPA_Correlation[upper.tri(DPA_Correlation)]) > 0.75))
## [1] 8
if (DPA_HighlyCorrelatedCount == 0) {
  print("No highly correlated predictors noted.")
} else {
  print(paste0("High correlation observed for ",
               (DPA_HighlyCorrelatedCount),
               " pairs of numeric variable(s) with Correlation.Coefficient>0.75."))
  
  (DPA_HighlyCorrelatedPairs <- corr_cross(DPA.Predictors.Numeric,
  max_pvalue = 0.05, 
  top = DPA_HighlyCorrelatedCount,
  rm.na = TRUE,
  grid = FALSE
))
  
}
## [1] "High correlation observed for 8 pairs of numeric variable(s) with Correlation.Coefficient>0.75."

if (DPA_HighlyCorrelatedCount > 0) {
  DPA_HighlyCorrelated <- findCorrelation(DPA_Correlation, cutoff = 0.75)
  
  (DPA_HighlyCorrelatedForRemoval <- length(DPA_HighlyCorrelated))
  
  print(paste0("High correlation can be resolved by removing ",
               (DPA_HighlyCorrelatedForRemoval),
               " numeric variable(s)."))
  
  for (j in 1:DPA_HighlyCorrelatedForRemoval) {
  DPA_HighlyCorrelatedRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_HighlyCorrelated[j]]
  print(paste0("Variable ",
               j,
               " for removal: ",
               DPA_HighlyCorrelatedRemovedVariable))
  }
  
}
## [1] "High correlation can be resolved by removing 6 numeric variable(s)."
## [1] "Variable 1 for removal: INFMOR"
## [1] "Variable 2 for removal: CLTECH"
## [1] "Variable 3 for removal: URBPOP"
## [1] "Variable 4 for removal: MEAIMM"
## [1] "Variable 5 for removal: DPTIMM"
## [1] "Variable 6 for removal: GNI"

1.3.3.4 Linear Dependencies


Code Chunk | Output
##################################
# Linear Dependencies
##################################

##################################
# Finding linear dependencies
##################################
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)

##################################
# Identifying the linearly dependent variables
##################################
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)

(DPA_LinearlyDependentCount <- length(DPA_LinearlyDependent$linearCombos))
## [1] 0
if (DPA_LinearlyDependentCount == 0) {
  print("No linearly dependent predictors noted.")
} else {
  print(paste0("Linear dependency observed for ",
               (DPA_LinearlyDependentCount),
               " subset(s) of numeric variable(s)."))
  
  for (i in 1:DPA_LinearlyDependentCount) {
    DPA_LinearlyDependentSubset <- colnames(DPA.Predictors.Numeric)[DPA_LinearlyDependent$linearCombos[[i]]]
    print(paste0("Linear dependent variable(s) for subset ",
                 i,
                 " include: ",
                 DPA_LinearlyDependentSubset))
  }
  
}
## [1] "No linearly dependent predictors noted."
##################################
# Identifying the linearly dependent variables for removal
##################################

if (DPA_LinearlyDependentCount > 0) {
  DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
  
  DPA_LinearlyDependentForRemoval <- length(DPA_LinearlyDependent$remove)
  
  print(paste0("Linear dependency can be resolved by removing ",
               (DPA_LinearlyDependentForRemoval),
               " numeric variable(s)."))
  
  for (j in 1:DPA_LinearlyDependentForRemoval) {
  DPA_LinearlyDependentRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_LinearlyDependent$remove[j]]
  print(paste0("Variable ",
               j,
               " for removal: ",
               DPA_LinearlyDependentRemovedVariable))
  }

}

1.3.3.5 Shape Transformation


Code Chunk | Output
##################################
# Shape Transformation
##################################

##################################
# Applying a Box-Cox transformation
##################################
DPA_BoxCox <- preProcess(DPA.Predictors.Numeric, method = c("BoxCox"))
DPA_BoxCoxTransformed <- predict(DPA_BoxCox, DPA.Predictors.Numeric)

for (i in 1:ncol(DPA_BoxCoxTransformed)) {
  Median <- format(round(median(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
  Mean <- format(round(mean(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
  Skewness <- format(round(skewness(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
  print(
  ggplot(DPA_BoxCoxTransformed, aes(x=DPA_BoxCoxTransformed[,i])) +
  geom_histogram(binwidth=1,color="black", fill="white") +
  geom_vline(aes(xintercept=mean(DPA_BoxCoxTransformed[,i])),
            color="blue", size=1) +
    geom_vline(aes(xintercept=median(DPA_BoxCoxTransformed[,i])),
            color="red", size=1) +
  theme_bw() +
  ylab("Count") +
  xlab(names(DPA_BoxCoxTransformed)[i]) +
  labs(title=names(DPA_BoxCoxTransformed)[i],
       subtitle=paste0("Median = ", Median,
                       ", Mean = ", Mean,
                       ", Skewness = ", Skewness)))
}

DPA_BoxCoxTransformed <- cbind(DPA_BoxCoxTransformed,DPA[,c("COUNTRY",
                                                            "YEAR",
                                                            "GENDER",
                                                            "CONTIN",
                                                            "LIFEXP")])

1.3.3.6 Pre-Processed Dataset


Code Chunk | Output
##################################
# Creating the pre-modelling
# train set
##################################
PMA <- DPA_BoxCoxTransformed[,!names(DPA_BoxCoxTransformed) %in% c("YEAR",
                                                                   "GNI",
                                                                   "DPTIMM",
                                                                   "MEAIMM",
                                                                   "RURPOP",
                                                                   "SANSER",
                                                                   "RTIMOR")]

##################################
# Gathering descriptive statistics
##################################
(PMA_Skimmed <- skim(PMA))
Data summary
Name PMA
Number of rows 364
Number of columns 16
_______________________
Column type frequency:
character 1
factor 2
numeric 13
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
COUNTRY 0 1 4 30 0 182 0

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
GENDER 0 1 FALSE 2 Mal: 182, Fem: 182
CONTIN 0 1 FALSE 6 Afr: 106, Asi: 94, Eur: 78, Nor: 42

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
UNEMPR 0 1 2.13 1.21 -2.05 1.42 2.00 2.90 5.26 ▁▂▇▅▂
INFMOR 0 1 2.56 1.06 0.34 1.70 2.63 3.49 4.49 ▅▆▇▇▆
GDP 0 1 3.85 2.21 -1.67 2.55 3.76 5.53 9.97 ▂▇▇▆▁
CLTECH 0 1 66.08 37.77 0.00 30.30 83.95 100.00 100.00 ▃▁▁▂▇
PERCAP 0 1 1.75 1.40 -1.48 0.65 1.80 2.82 4.73 ▂▆▇▆▃
TUBINC 0 1 106.32 137.70 0.00 12.00 46.00 150.00 654.00 ▇▂▁▁▁
HEPIMM 0 1 3877.07 1006.71 612.00 3304.98 4231.50 4704.00 4900.00 ▁▁▁▃▇
HOSBED 0 1 0.76 0.86 -1.61 0.14 0.83 1.40 2.62 ▁▅▇▇▃
TUBTRT 0 1 77.92 17.31 0.00 73.00 83.00 88.00 100.00 ▁▁▁▅▇
URBPOP 0 1 58.27 22.68 13.25 40.24 58.36 77.38 100.00 ▅▆▇▇▅
NCOMOR 0 1 4.67 1.08 1.87 3.93 4.72 5.38 7.96 ▂▅▇▃▁
SUIRAT 0 1 9.49 10.91 0.00 3.18 6.25 11.80 116.00 ▇▁▁▁▁
LIFEXP 0 1 72.50 7.77 51.20 66.93 73.53 78.54 87.45 ▁▃▆▇▅

1.3.4 Data Exploration


Code Chunk | Output
##################################
# Loading dataset
##################################
PME <- PMA
PME.Numeric <- PME[,sapply(PME, is.numeric), drop = FALSE]

##################################
# Listing all Predictors
##################################
PME.Predictors <- PME[,!names(PME) %in% c("COUNTRY","LIFEXP")]

##################################
# Listing all numeric Predictors
##################################
PME.Predictors.Numeric <- PME.Predictors[,sapply(PME.Predictors, is.numeric), drop = FALSE]
ncol(PME.Predictors.Numeric)
## [1] 12
##################################
# Listing all numeric Predictors
##################################
PME.Predictors.Factor <- PME.Predictors[,sapply(PME.Predictors, is.factor), drop = FALSE]
ncol(PME.Predictors.Factor)
## [1] 2
##################################
# Formulating the scatter plot
##################################
featurePlot(x = PME.Predictors.Numeric, 
            y = PME$LIFEXP,
            plot = "scatter",
            type = c("p", "smooth"),
            span = .5,
            layout = c(4, 3))

##################################
# Formulating the box plot
##################################
featurePlot(x = PME.Numeric, 
            y = PME$GENDER,
            plot = "box",
            scales = list(x = list(relation="free", rot = 90), 
                          y = list(relation="free")),
            adjust = 1.5,
            layout = c(4, 4))

##################################
# Formulating the box plot
##################################
featurePlot(x = PME.Numeric, 
            y = PME$CONTIN,
            plot = "box",
            scales = list(x = list(relation="free", rot = 90), 
                          y = list(relation="free")),
            adjust = 1.5,
            layout = c(4, 4))

1.3.5 Feature Selection


1.3.5.1 Locally Weighted Scatterplot Smoothing Pseudo-R-Squared (LOWESSPR)


Code Chunk | Output
##################################
# Evaluating model-independent
# feature importance metrics
##################################

##################################
# Obtaining the LOWESSPR pseudo-R-Squared
##################################
FE_LOWESSPR <- filterVarImp(x = PME.Numeric[,!names(PME.Numeric) %in% c("LIFEXP")],
                            y = PME$LIFEXP,
                            nonpara = TRUE)

##################################
# Formulating the summary table
##################################
FE_LOWESSPR_Summary <- FE_LOWESSPR 

FE_LOWESSPR_Summary$Predictor <- rownames(FE_LOWESSPR)
names(FE_LOWESSPR_Summary)[1] <- "LOWESSPR"
FE_LOWESSPR_Summary$Metric <- rep("LOWESSPR",nrow(FE_LOWESSPR))

FE_LOWESSPR_Summary
##          LOWESSPR Predictor   Metric
## UNEMPR 0.03707208    UNEMPR LOWESSPR
## INFMOR 0.82546121    INFMOR LOWESSPR
## GDP    0.25427692       GDP LOWESSPR
## CLTECH 0.58186548    CLTECH LOWESSPR
## PERCAP 0.62170474    PERCAP LOWESSPR
## TUBINC 0.51521923    TUBINC LOWESSPR
## HEPIMM 0.18618812    HEPIMM LOWESSPR
## HOSBED 0.34642501    HOSBED LOWESSPR
## TUBTRT 0.10267243    TUBTRT LOWESSPR
## URBPOP 0.35022753    URBPOP LOWESSPR
## NCOMOR 0.59655877    NCOMOR LOWESSPR
## SUIRAT 0.06256602    SUIRAT LOWESSPR
##################################
# Exploring predictor performance
# using LOWESS
##################################
dotplot(Predictor ~ LOWESSPR | Metric, 
        FE_LOWESSPR_Summary,
        origin = 0,
        type = c("p", "h"),
        pch = 16,
        cex = 2,
        alpha = 0.45,
        prepanel = function(x, y) {
            list(ylim = levels(reorder(y, x)))
        },
        panel = function(x, y, ...) {
            panel.dotplot(x, reorder(y, x), ...)
        })


1.3.5.2 Pearson’s Correlation Coefficient (PCC)


Code Chunk | Output
##################################
# Obtaining the Pearson correlation coefficient
##################################
(FE_PCC <- abs(cor(PME.Numeric, method="pearson")[-13,13]))
##     UNEMPR     INFMOR        GDP     CLTECH     PERCAP     TUBINC     HEPIMM 
## 0.01518356 0.87964210 0.45844159 0.75234946 0.78523930 0.58919807 0.43149521 
##     HOSBED     TUBTRT     URBPOP     NCOMOR     SUIRAT 
## 0.56281916 0.32042539 0.57322144 0.73325984 0.17867507
##################################
# Formulating the summary table
##################################
FE_PCC_Summary <- data.frame(Predictor = names(PME.Numeric)[1:(ncol(PME.Numeric)-1)],
                             PCC = FE_PCC,
                             Metric = rep("PCC", length(FE_PCC)))

FE_PCC_Summary
##        Predictor        PCC Metric
## UNEMPR    UNEMPR 0.01518356    PCC
## INFMOR    INFMOR 0.87964210    PCC
## GDP          GDP 0.45844159    PCC
## CLTECH    CLTECH 0.75234946    PCC
## PERCAP    PERCAP 0.78523930    PCC
## TUBINC    TUBINC 0.58919807    PCC
## HEPIMM    HEPIMM 0.43149521    PCC
## HOSBED    HOSBED 0.56281916    PCC
## TUBTRT    TUBTRT 0.32042539    PCC
## URBPOP    URBPOP 0.57322144    PCC
## NCOMOR    NCOMOR 0.73325984    PCC
## SUIRAT    SUIRAT 0.17867507    PCC
##################################
# Exploring predictor performance
# using PCC
##################################
dotplot(Predictor ~ PCC | Metric, 
        FE_PCC_Summary,
        origin = 0,
        type = c("p", "h"),
        pch = 16,
        cex = 2,
        alpha = 0.45,
        prepanel = function(x, y) {
            list(ylim = levels(reorder(y, x)))
        },
        panel = function(x, y, ...) {
            panel.dotplot(x, reorder(y, x), ...)
        })


1.3.5.3 Spearman’s Rank Correlation Coefficient (SRCC)


Code Chunk | Output
##################################
# Obtaining the Spearman's rank correlation coefficient
##################################
(FE_SRCC <- abs(cor(PME.Numeric, method="spearman")[-13,13]))
##      UNEMPR      INFMOR         GDP      CLTECH      PERCAP      TUBINC 
## 0.003729075 0.891871299 0.499048543 0.783732221 0.798156088 0.716251344 
##      HEPIMM      HOSBED      TUBTRT      URBPOP      NCOMOR      SUIRAT 
## 0.382149849 0.555871706 0.332516893 0.600468144 0.789128052 0.124188611
##################################
# Formulating the summary table
##################################
FE_SRCC_Summary <- data.frame(Predictor = names(PME.Numeric)[1:(ncol(PME.Numeric)-1)],
                             SRCC = FE_SRCC,
                             Metric = rep("SRCC", length(FE_SRCC)))

FE_SRCC_Summary
##        Predictor        SRCC Metric
## UNEMPR    UNEMPR 0.003729075   SRCC
## INFMOR    INFMOR 0.891871299   SRCC
## GDP          GDP 0.499048543   SRCC
## CLTECH    CLTECH 0.783732221   SRCC
## PERCAP    PERCAP 0.798156088   SRCC
## TUBINC    TUBINC 0.716251344   SRCC
## HEPIMM    HEPIMM 0.382149849   SRCC
## HOSBED    HOSBED 0.555871706   SRCC
## TUBTRT    TUBTRT 0.332516893   SRCC
## URBPOP    URBPOP 0.600468144   SRCC
## NCOMOR    NCOMOR 0.789128052   SRCC
## SUIRAT    SUIRAT 0.124188611   SRCC
##################################
# Exploring predictor performance
# using SRCC
##################################
dotplot(Predictor ~ SRCC | Metric, 
        FE_SRCC_Summary,
        origin = 0,
        type = c("p", "h"),
        pch = 16,
        cex = 2,
        alpha = 0.45,
        prepanel = function(x, y) {
            list(ylim = levels(reorder(y, x)))
        },
        panel = function(x, y, ...) {
            panel.dotplot(x, reorder(y, x), ...)
        })


1.3.5.4 Maximal Information Coefficient (MIC)


Code Chunk | Output
##################################
# Obtaining the maximal information coefficient
##################################
FE_MIC <- mine(x = PME.Numeric[,!names(PME.Numeric) %in% c("LIFEXP")],
               y = PME$LIFEXP)$MIC

##################################
# Formulating the summary table
##################################
FE_MIC_Summary <- data.frame(Predictor = names(PME.Numeric)[1:(ncol(PME.Numeric)-1)],
                             MIC = FE_MIC[,1],
                             Metric = rep("MIC", length(FE_MIC)))

FE_MIC_Summary
##    Predictor       MIC Metric
## 1     UNEMPR 0.1911950    MIC
## 2     INFMOR 0.7083938    MIC
## 3        GDP 0.3234249    MIC
## 4     CLTECH 0.5099735    MIC
## 5     PERCAP 0.5502257    MIC
## 6     TUBINC 0.5121849    MIC
## 7     HEPIMM 0.2652364    MIC
## 8     HOSBED 0.3711493    MIC
## 9     TUBTRT 0.2356973    MIC
## 10    URBPOP 0.4097827    MIC
## 11    NCOMOR 0.6438989    MIC
## 12    SUIRAT 0.2325372    MIC
##################################
# Exploring predictor performance
# using MIC
##################################
dotplot(Predictor ~ MIC | Metric, 
        FE_MIC_Summary,
        origin = 0,
        type = c("p", "h"),
        pch = 16,
        cex = 2,
        alpha = 0.45,
        prepanel = function(x, y) {
            list(ylim = levels(reorder(y, x)))
        },
        panel = function(x, y, ...) {
            panel.dotplot(x, reorder(y, x), ...)
        })


1.3.5.5 Relief Values (RV)


Code Chunk | Output
##################################
# Obtaining the relief values
##################################
FE_RV <- attrEval(LIFEXP ~ .,  
                  data = PME.Numeric,
                  estimator = "RReliefFequalK")

##################################
# Formulating the summary table
##################################
FE_RV_Summary <- data.frame(Predictor = names(FE_RV),
                            RV = FE_RV,
                            Metric = rep("RV", length(FE_RV)))

FE_RV_Summary
##        Predictor           RV Metric
## UNEMPR    UNEMPR  0.007050804     RV
## INFMOR    INFMOR  0.090032432     RV
## GDP          GDP -0.107294188     RV
## CLTECH    CLTECH  0.042070458     RV
## PERCAP    PERCAP -0.042064701     RV
## TUBINC    TUBINC  0.073890249     RV
## HEPIMM    HEPIMM -0.039606463     RV
## HOSBED    HOSBED -0.062080020     RV
## TUBTRT    TUBTRT -0.155115571     RV
## URBPOP    URBPOP -0.105766623     RV
## NCOMOR    NCOMOR  0.268209813     RV
## SUIRAT    SUIRAT  0.104271800     RV
##################################
# Exploring predictor performance
##################################
dotplot(Predictor ~ RV | Metric, 
        FE_RV_Summary,
        origin = 0,
        type = c("p", "h"),
        pch = 16,
        cex = 2,
        alpha = 0.45,
        prepanel = function(x, y) {
            list(ylim = levels(reorder(y, x)))
        },
        panel = function(x, y, ...) {
            panel.dotplot(x, reorder(y, x), ...)
        })

1.3.6 Model Development and Performance Estimation


1.3.6.1 Stochastic Gradient Boosting (GBM)


Code Chunk | Output
##################################
# Preparing the dataset for
# model development and test
##################################
set.seed(12345678)
trainIndex <- createDataPartition(PME$LIFEXP,
                                  p = 0.8, 
                                  list = FALSE, 
                                  times = 1)

##################################
# Formulating the model development data
##################################
MD <- PME[ trainIndex,]

##################################
# Formulating the model test data
##################################
MT <- PME[-trainIndex,]

##################################
# Preparing the dataset for
# model development
##################################
MD <- MD[,c("GENDER","CONTIN","INFMOR","PERCAP","CLTECH","NCOMOR","LIFEXP")]

MD.Model.Predictors <- MD[,c("GENDER","CONTIN","INFMOR","PERCAP","CLTECH","NCOMOR")]

##################################
# Preparing the dataset for
# model test
##################################
MT <- MT[,c("GENDER","CONTIN","INFMOR","PERCAP","CLTECH","NCOMOR","LIFEXP")]

MT.Model.Predictors <- MT[,c("GENDER","CONTIN","INFMOR","PERCAP","CLTECH","NCOMOR")]

##################################
# Creating consistent fold assignments
# for the 10-Fold Cross Validation process
##################################
set.seed(12345678)
KFold_Indices <- createFolds(MD$LIFEXP,
                             k = 10,
                             returnTrain=TRUE)
KFold_Control <- trainControl(method="cv",
                              index=KFold_Indices)

##################################
# Defining the model hyperparameter values
# for the GBM model
##################################
GBM_Grid = expand.grid(n.trees = c(100, 200, 300),
                       interaction.depth = c(1, 3, 5),
                       shrinkage = c(0.10,0.05,0.01),
                       n.minobsinnode = c(15,10,5))

##################################
# Running the GBM model
# by setting the caret method to 'gbm'
##################################
set.seed(12345678)
GBM_Tune <- train(x = MD.Model.Predictors,
                  y = MD$LIFEXP,
                  method = "gbm",
                  tuneGrid = GBM_Grid,
                  trControl = KFold_Control)
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.0192             nan     0.0100    0.7763
##      2       60.2652             nan     0.0100    0.7757
##      3       59.4737             nan     0.0100    0.7510
##      4       58.7221             nan     0.0100    0.7598
##      5       57.9938             nan     0.0100    0.7048
##      6       57.2211             nan     0.0100    0.7026
##      7       56.5150             nan     0.0100    0.6792
##      8       55.8126             nan     0.0100    0.7336
##      9       55.1307             nan     0.0100    0.6511
##     10       54.4439             nan     0.0100    0.6352
##     20       48.4888             nan     0.0100    0.4724
##     40       38.9299             nan     0.0100    0.4255
##     60       31.8093             nan     0.0100    0.2926
##     80       26.3714             nan     0.0100    0.2027
##    100       22.1975             nan     0.0100    0.1622
##    120       18.9764             nan     0.0100    0.1006
##    140       16.3808             nan     0.0100    0.0852
##    160       14.4027             nan     0.0100    0.0871
##    180       12.7414             nan     0.0100    0.0528
##    200       11.4125             nan     0.0100    0.0519
##    220       10.2835             nan     0.0100    0.0410
##    240        9.3387             nan     0.0100    0.0411
##    260        8.5430             nan     0.0100    0.0392
##    280        7.8728             nan     0.0100    0.0167
##    300        7.2959             nan     0.0100    0.0184
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.0182             nan     0.0100    0.8361
##      2       60.2779             nan     0.0100    0.8155
##      3       59.5017             nan     0.0100    0.7081
##      4       58.7437             nan     0.0100    0.6873
##      5       57.9931             nan     0.0100    0.7436
##      6       57.2779             nan     0.0100    0.6835
##      7       56.5913             nan     0.0100    0.7197
##      8       55.9075             nan     0.0100    0.6813
##      9       55.2119             nan     0.0100    0.6398
##     10       54.5418             nan     0.0100    0.6806
##     20       48.5210             nan     0.0100    0.5257
##     40       39.0515             nan     0.0100    0.3873
##     60       31.8911             nan     0.0100    0.2684
##     80       26.6252             nan     0.0100    0.2079
##    100       22.3697             nan     0.0100    0.1577
##    120       19.1586             nan     0.0100    0.1278
##    140       16.6712             nan     0.0100    0.0969
##    160       14.6273             nan     0.0100    0.0773
##    180       12.9450             nan     0.0100    0.0487
##    200       11.5525             nan     0.0100    0.0149
##    220       10.3859             nan     0.0100    0.0366
##    240        9.4442             nan     0.0100    0.0302
##    260        8.6273             nan     0.0100    0.0306
##    280        7.9543             nan     0.0100    0.0267
##    300        7.3607             nan     0.0100    0.0204
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.0556             nan     0.0100    0.7589
##      2       60.3011             nan     0.0100    0.7607
##      3       59.5410             nan     0.0100    0.7503
##      4       58.8573             nan     0.0100    0.6862
##      5       58.0926             nan     0.0100    0.7456
##      6       57.3219             nan     0.0100    0.6947
##      7       56.5769             nan     0.0100    0.7003
##      8       55.8379             nan     0.0100    0.6442
##      9       55.1650             nan     0.0100    0.6792
##     10       54.5170             nan     0.0100    0.6986
##     20       48.4557             nan     0.0100    0.5444
##     40       39.1432             nan     0.0100    0.3891
##     60       32.1819             nan     0.0100    0.2893
##     80       26.7656             nan     0.0100    0.2214
##    100       22.5205             nan     0.0100    0.1801
##    120       19.2565             nan     0.0100    0.1308
##    140       16.6481             nan     0.0100    0.1039
##    160       14.5722             nan     0.0100    0.0879
##    180       12.9101             nan     0.0100    0.0668
##    200       11.4789             nan     0.0100    0.0583
##    220       10.3831             nan     0.0100    0.0379
##    240        9.4585             nan     0.0100    0.0361
##    260        8.6801             nan     0.0100    0.0311
##    280        8.0069             nan     0.0100    0.0218
##    300        7.4189             nan     0.0100    0.0258
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.7811             nan     0.0100    0.9879
##      2       59.7756             nan     0.0100    1.0245
##      3       58.7235             nan     0.0100    0.9914
##      4       57.7344             nan     0.0100    0.9273
##      5       56.8135             nan     0.0100    0.9757
##      6       55.8527             nan     0.0100    0.8346
##      7       54.9932             nan     0.0100    0.8278
##      8       54.0432             nan     0.0100    0.9012
##      9       53.1906             nan     0.0100    0.8415
##     10       52.3483             nan     0.0100    0.8122
##     20       44.4018             nan     0.0100    0.7897
##     40       32.5229             nan     0.0100    0.4547
##     60       24.4382             nan     0.0100    0.2989
##     80       18.6018             nan     0.0100    0.2411
##    100       14.5876             nan     0.0100    0.1549
##    120       11.6671             nan     0.0100    0.1217
##    140        9.5683             nan     0.0100    0.0590
##    160        7.9564             nan     0.0100    0.0660
##    180        6.7628             nan     0.0100    0.0471
##    200        5.8981             nan     0.0100    0.0302
##    220        5.2091             nan     0.0100    0.0163
##    240        4.7261             nan     0.0100    0.0181
##    260        4.3201             nan     0.0100    0.0122
##    280        4.0142             nan     0.0100    0.0040
##    300        3.7828             nan     0.0100    0.0046
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.7363             nan     0.0100    0.9393
##      2       59.7677             nan     0.0100    0.9532
##      3       58.8018             nan     0.0100    0.9275
##      4       57.8559             nan     0.0100    0.9172
##      5       56.8602             nan     0.0100    1.0358
##      6       55.8760             nan     0.0100    0.9584
##      7       54.9599             nan     0.0100    0.9441
##      8       54.0550             nan     0.0100    0.8263
##      9       53.1614             nan     0.0100    0.8152
##     10       52.3250             nan     0.0100    0.9347
##     20       44.3860             nan     0.0100    0.7844
##     40       32.2747             nan     0.0100    0.4327
##     60       24.1659             nan     0.0100    0.3232
##     80       18.3571             nan     0.0100    0.2159
##    100       14.3483             nan     0.0100    0.1701
##    120       11.5081             nan     0.0100    0.1229
##    140        9.4528             nan     0.0100    0.0710
##    160        7.9033             nan     0.0100    0.0642
##    180        6.7763             nan     0.0100    0.0325
##    200        5.9195             nan     0.0100    0.0272
##    220        5.2733             nan     0.0100    0.0241
##    240        4.7617             nan     0.0100    0.0130
##    260        4.4171             nan     0.0100    0.0155
##    280        4.1279             nan     0.0100    0.0100
##    300        3.8959             nan     0.0100    0.0051
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.7823             nan     0.0100    0.9654
##      2       59.8273             nan     0.0100    0.9559
##      3       58.8419             nan     0.0100    1.0246
##      4       57.8789             nan     0.0100    0.8718
##      5       56.9219             nan     0.0100    0.8905
##      6       55.9560             nan     0.0100    0.9847
##      7       55.0535             nan     0.0100    0.8720
##      8       54.1138             nan     0.0100    0.8692
##      9       53.2551             nan     0.0100    0.9301
##     10       52.4563             nan     0.0100    0.8490
##     20       44.6100             nan     0.0100    0.7685
##     40       32.8376             nan     0.0100    0.4628
##     60       24.6223             nan     0.0100    0.3640
##     80       18.8576             nan     0.0100    0.2061
##    100       14.7512             nan     0.0100    0.1434
##    120       11.8363             nan     0.0100    0.0932
##    140        9.6990             nan     0.0100    0.1010
##    160        8.1543             nan     0.0100    0.0668
##    180        6.9782             nan     0.0100    0.0413
##    200        6.0937             nan     0.0100    0.0303
##    220        5.4293             nan     0.0100    0.0227
##    240        4.9297             nan     0.0100    0.0183
##    260        4.5575             nan     0.0100    0.0107
##    280        4.2786             nan     0.0100    0.0083
##    300        4.0603             nan     0.0100    0.0037
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.7301             nan     0.0100    1.0291
##      2       59.6424             nan     0.0100    1.0195
##      3       58.5884             nan     0.0100    0.8919
##      4       57.5610             nan     0.0100    0.8836
##      5       56.5464             nan     0.0100    1.0641
##      6       55.5833             nan     0.0100    0.8985
##      7       54.6208             nan     0.0100    0.8715
##      8       53.6729             nan     0.0100    0.8315
##      9       52.7461             nan     0.0100    0.9069
##     10       51.8164             nan     0.0100    0.8645
##     20       43.5196             nan     0.0100    0.7079
##     40       31.1126             nan     0.0100    0.5218
##     60       22.6816             nan     0.0100    0.3357
##     80       16.9224             nan     0.0100    0.2170
##    100       12.8284             nan     0.0100    0.1488
##    120       10.0438             nan     0.0100    0.1165
##    140        7.9798             nan     0.0100    0.0732
##    160        6.5047             nan     0.0100    0.0622
##    180        5.4528             nan     0.0100    0.0331
##    200        4.7095             nan     0.0100    0.0304
##    220        4.1500             nan     0.0100    0.0162
##    240        3.7521             nan     0.0100    0.0085
##    260        3.4586             nan     0.0100    0.0048
##    280        3.2304             nan     0.0100    0.0034
##    300        3.0364             nan     0.0100    0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.7447             nan     0.0100    1.0219
##      2       59.7039             nan     0.0100    1.0660
##      3       58.6502             nan     0.0100    1.0398
##      4       57.6297             nan     0.0100    1.0546
##      5       56.6425             nan     0.0100    1.0440
##      6       55.6402             nan     0.0100    1.0016
##      7       54.6664             nan     0.0100    0.9037
##      8       53.7131             nan     0.0100    0.9369
##      9       52.7940             nan     0.0100    0.9479
##     10       51.8332             nan     0.0100    0.9079
##     20       43.5255             nan     0.0100    0.7096
##     40       31.1589             nan     0.0100    0.4859
##     60       22.7958             nan     0.0100    0.3179
##     80       16.9616             nan     0.0100    0.2104
##    100       12.9139             nan     0.0100    0.1560
##    120       10.0784             nan     0.0100    0.1178
##    140        8.0651             nan     0.0100    0.0728
##    160        6.6455             nan     0.0100    0.0470
##    180        5.6044             nan     0.0100    0.0368
##    200        4.8571             nan     0.0100    0.0313
##    220        4.3103             nan     0.0100    0.0125
##    240        3.9046             nan     0.0100    0.0112
##    260        3.5932             nan     0.0100    0.0096
##    280        3.3656             nan     0.0100    0.0025
##    300        3.1936             nan     0.0100    0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.7644             nan     0.0100    1.0981
##      2       59.6868             nan     0.0100    1.0994
##      3       58.6746             nan     0.0100    1.0847
##      4       57.6006             nan     0.0100    1.1392
##      5       56.6122             nan     0.0100    0.9589
##      6       55.6823             nan     0.0100    1.0254
##      7       54.6742             nan     0.0100    1.0423
##      8       53.7607             nan     0.0100    0.9721
##      9       52.8951             nan     0.0100    0.9395
##     10       52.0213             nan     0.0100    0.9194
##     20       43.8354             nan     0.0100    0.6542
##     40       31.4738             nan     0.0100    0.5421
##     60       23.1368             nan     0.0100    0.3651
##     80       17.2716             nan     0.0100    0.2035
##    100       13.1896             nan     0.0100    0.1619
##    120       10.3864             nan     0.0100    0.1076
##    140        8.4033             nan     0.0100    0.0712
##    160        6.9817             nan     0.0100    0.0588
##    180        6.0054             nan     0.0100    0.0388
##    200        5.2517             nan     0.0100    0.0227
##    220        4.6931             nan     0.0100    0.0165
##    240        4.3071             nan     0.0100    0.0064
##    260        4.0008             nan     0.0100    0.0025
##    280        3.7852             nan     0.0100    0.0075
##    300        3.6251             nan     0.0100    0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.0344             nan     0.0500    3.8415
##      2       54.5365             nan     0.0500    3.4273
##      3       51.7720             nan     0.0500    3.1884
##      4       49.0894             nan     0.0500    2.7612
##      5       46.2533             nan     0.0500    2.6837
##      6       43.7354             nan     0.0500    2.4708
##      7       41.2809             nan     0.0500    2.1635
##      8       39.2262             nan     0.0500    2.1419
##      9       37.0853             nan     0.0500    2.2079
##     10       35.1301             nan     0.0500    1.9341
##     20       22.0217             nan     0.0500    0.6677
##     40       11.3559             nan     0.0500    0.2919
##     60        7.2935             nan     0.0500    0.1375
##     80        5.4364             nan     0.0500    0.0672
##    100        4.5723             nan     0.0500   -0.0144
##    120        4.1502             nan     0.0500    0.0075
##    140        3.9273             nan     0.0500   -0.0055
##    160        3.7719             nan     0.0500   -0.0000
##    180        3.6672             nan     0.0500   -0.0064
##    200        3.5875             nan     0.0500   -0.0060
##    220        3.5093             nan     0.0500   -0.0159
##    240        3.4362             nan     0.0500   -0.0065
##    260        3.3752             nan     0.0500   -0.0091
##    280        3.3165             nan     0.0500   -0.0038
##    300        3.2673             nan     0.0500   -0.0031
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.9628             nan     0.0500    3.7315
##      2       54.6727             nan     0.0500    3.4527
##      3       51.5215             nan     0.0500    3.1737
##      4       48.4679             nan     0.0500    2.9660
##      5       45.6125             nan     0.0500    2.6173
##      6       43.1555             nan     0.0500    2.3758
##      7       40.6447             nan     0.0500    2.3076
##      8       38.6491             nan     0.0500    1.7343
##      9       36.8462             nan     0.0500    1.6289
##     10       35.3466             nan     0.0500    1.4689
##     20       22.3990             nan     0.0500    0.4800
##     40       11.4911             nan     0.0500    0.2501
##     60        7.4326             nan     0.0500    0.1619
##     80        5.5370             nan     0.0500    0.0593
##    100        4.6345             nan     0.0500    0.0070
##    120        4.1969             nan     0.0500    0.0073
##    140        3.9968             nan     0.0500    0.0072
##    160        3.8581             nan     0.0500   -0.0145
##    180        3.7538             nan     0.0500   -0.0117
##    200        3.6598             nan     0.0500   -0.0090
##    220        3.5888             nan     0.0500   -0.0061
##    240        3.5186             nan     0.0500   -0.0077
##    260        3.4551             nan     0.0500   -0.0162
##    280        3.4013             nan     0.0500   -0.0084
##    300        3.3618             nan     0.0500   -0.0169
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.6228             nan     0.0500    3.9987
##      2       54.1330             nan     0.0500    3.3888
##      3       51.0764             nan     0.0500    2.9593
##      4       48.2039             nan     0.0500    2.5425
##      5       45.4965             nan     0.0500    2.7583
##      6       42.7987             nan     0.0500    2.4621
##      7       40.5409             nan     0.0500    2.2391
##      8       38.5091             nan     0.0500    2.0108
##      9       36.5919             nan     0.0500    2.1784
##     10       34.6881             nan     0.0500    1.8360
##     20       21.8050             nan     0.0500    0.8761
##     40       11.3694             nan     0.0500    0.2106
##     60        7.4091             nan     0.0500    0.0972
##     80        5.6757             nan     0.0500    0.0369
##    100        4.7825             nan     0.0500    0.0123
##    120        4.3704             nan     0.0500   -0.0012
##    140        4.1583             nan     0.0500   -0.0018
##    160        4.0575             nan     0.0500   -0.0117
##    180        3.9493             nan     0.0500   -0.0036
##    200        3.8721             nan     0.0500   -0.0035
##    220        3.7928             nan     0.0500   -0.0082
##    240        3.7154             nan     0.0500   -0.0074
##    260        3.6505             nan     0.0500   -0.0039
##    280        3.5909             nan     0.0500   -0.0066
##    300        3.5425             nan     0.0500   -0.0068
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.5954             nan     0.0500    5.0099
##      2       51.9922             nan     0.0500    4.5935
##      3       47.6779             nan     0.0500    3.7401
##      4       43.9259             nan     0.0500    4.2016
##      5       40.4580             nan     0.0500    3.1794
##      6       37.3158             nan     0.0500    2.5898
##      7       34.4163             nan     0.0500    2.8136
##      8       31.9373             nan     0.0500    2.3894
##      9       29.6482             nan     0.0500    2.1312
##     10       27.6422             nan     0.0500    2.1355
##     20       14.0346             nan     0.0500    0.6351
##     40        5.8839             nan     0.0500    0.1299
##     60        3.7923             nan     0.0500    0.0253
##     80        3.1408             nan     0.0500   -0.0048
##    100        2.8130             nan     0.0500   -0.0304
##    120        2.5901             nan     0.0500   -0.0085
##    140        2.3987             nan     0.0500   -0.0198
##    160        2.2314             nan     0.0500   -0.0156
##    180        2.0850             nan     0.0500   -0.0075
##    200        1.9742             nan     0.0500   -0.0069
##    220        1.8957             nan     0.0500   -0.0090
##    240        1.8090             nan     0.0500   -0.0129
##    260        1.7282             nan     0.0500   -0.0128
##    280        1.6537             nan     0.0500   -0.0055
##    300        1.5882             nan     0.0500   -0.0053
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.7704             nan     0.0500    5.1293
##      2       52.0636             nan     0.0500    4.1257
##      3       47.9781             nan     0.0500    3.9736
##      4       44.2478             nan     0.0500    3.7691
##      5       41.0279             nan     0.0500    3.4203
##      6       37.9647             nan     0.0500    2.9650
##      7       35.0765             nan     0.0500    2.9375
##      8       32.3973             nan     0.0500    2.5871
##      9       30.1281             nan     0.0500    2.3746
##     10       28.0278             nan     0.0500    2.2176
##     20       14.2885             nan     0.0500    0.8064
##     40        5.8812             nan     0.0500    0.1339
##     60        3.9463             nan     0.0500    0.0309
##     80        3.3117             nan     0.0500   -0.0012
##    100        3.0017             nan     0.0500    0.0001
##    120        2.8043             nan     0.0500   -0.0145
##    140        2.6628             nan     0.0500   -0.0136
##    160        2.5457             nan     0.0500   -0.0163
##    180        2.4306             nan     0.0500   -0.0114
##    200        2.3195             nan     0.0500   -0.0038
##    220        2.2270             nan     0.0500   -0.0206
##    240        2.1431             nan     0.0500   -0.0173
##    260        2.0566             nan     0.0500   -0.0136
##    280        1.9931             nan     0.0500   -0.0136
##    300        1.9436             nan     0.0500   -0.0095
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.5645             nan     0.0500    5.1224
##      2       51.9284             nan     0.0500    3.8676
##      3       48.0714             nan     0.0500    3.8581
##      4       44.3610             nan     0.0500    3.8282
##      5       40.8656             nan     0.0500    3.4710
##      6       37.6051             nan     0.0500    3.2033
##      7       34.8928             nan     0.0500    2.7928
##      8       32.3792             nan     0.0500    2.3185
##      9       30.1353             nan     0.0500    2.5385
##     10       28.0992             nan     0.0500    1.9273
##     20       14.5747             nan     0.0500    0.7630
##     40        5.9520             nan     0.0500    0.1248
##     60        4.1205             nan     0.0500    0.0301
##     80        3.5689             nan     0.0500   -0.0043
##    100        3.2480             nan     0.0500   -0.0127
##    120        3.0308             nan     0.0500   -0.0122
##    140        2.8719             nan     0.0500   -0.0176
##    160        2.7390             nan     0.0500   -0.0111
##    180        2.6283             nan     0.0500   -0.0092
##    200        2.5264             nan     0.0500   -0.0211
##    220        2.4331             nan     0.0500   -0.0157
##    240        2.3688             nan     0.0500   -0.0182
##    260        2.2677             nan     0.0500   -0.0081
##    280        2.2001             nan     0.0500   -0.0168
##    300        2.1255             nan     0.0500   -0.0103
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.5588             nan     0.0500    4.7218
##      2       51.5893             nan     0.0500    4.9800
##      3       47.1904             nan     0.0500    4.5704
##      4       43.1887             nan     0.0500    3.4471
##      5       39.5545             nan     0.0500    3.8226
##      6       36.4772             nan     0.0500    3.1387
##      7       33.6011             nan     0.0500    2.6734
##      8       30.9170             nan     0.0500    2.4560
##      9       28.6029             nan     0.0500    2.2412
##     10       26.5061             nan     0.0500    2.0475
##     20       12.6805             nan     0.0500    0.8432
##     40        4.6380             nan     0.0500    0.0789
##     60        3.0019             nan     0.0500    0.0182
##     80        2.4495             nan     0.0500   -0.0166
##    100        2.1448             nan     0.0500   -0.0199
##    120        1.9072             nan     0.0500   -0.0127
##    140        1.7133             nan     0.0500   -0.0077
##    160        1.5620             nan     0.0500   -0.0124
##    180        1.4178             nan     0.0500   -0.0161
##    200        1.3135             nan     0.0500   -0.0071
##    220        1.2133             nan     0.0500   -0.0081
##    240        1.1287             nan     0.0500   -0.0124
##    260        1.0543             nan     0.0500   -0.0106
##    280        0.9757             nan     0.0500   -0.0066
##    300        0.9178             nan     0.0500   -0.0085
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.5683             nan     0.0500    4.9344
##      2       51.6944             nan     0.0500    4.8168
##      3       47.2959             nan     0.0500    4.3846
##      4       43.5104             nan     0.0500    3.7302
##      5       39.7695             nan     0.0500    3.7444
##      6       36.6010             nan     0.0500    3.1937
##      7       33.6638             nan     0.0500    2.6948
##      8       30.9358             nan     0.0500    2.6595
##      9       28.4562             nan     0.0500    2.1274
##     10       26.2043             nan     0.0500    2.0773
##     20       12.7507             nan     0.0500    0.6586
##     40        4.8954             nan     0.0500    0.1259
##     60        3.2180             nan     0.0500   -0.0104
##     80        2.7147             nan     0.0500   -0.0113
##    100        2.4543             nan     0.0500   -0.0134
##    120        2.2894             nan     0.0500   -0.0185
##    140        2.1054             nan     0.0500   -0.0231
##    160        1.9550             nan     0.0500   -0.0053
##    180        1.8195             nan     0.0500   -0.0263
##    200        1.6884             nan     0.0500   -0.0134
##    220        1.5804             nan     0.0500   -0.0132
##    240        1.4846             nan     0.0500   -0.0204
##    260        1.4026             nan     0.0500   -0.0103
##    280        1.3298             nan     0.0500   -0.0055
##    300        1.2638             nan     0.0500   -0.0054
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.2670             nan     0.0500    4.9296
##      2       51.4201             nan     0.0500    4.4277
##      3       47.1252             nan     0.0500    4.1616
##      4       43.0992             nan     0.0500    4.4701
##      5       39.5475             nan     0.0500    3.2675
##      6       36.3316             nan     0.0500    2.9151
##      7       33.4274             nan     0.0500    2.7498
##      8       30.8849             nan     0.0500    2.4451
##      9       28.5181             nan     0.0500    2.3685
##     10       26.2967             nan     0.0500    2.2567
##     20       12.7991             nan     0.0500    0.7365
##     40        5.1023             nan     0.0500    0.1075
##     60        3.5319             nan     0.0500    0.0196
##     80        3.0871             nan     0.0500   -0.0160
##    100        2.8119             nan     0.0500   -0.0116
##    120        2.6216             nan     0.0500   -0.0075
##    140        2.4403             nan     0.0500   -0.0120
##    160        2.3001             nan     0.0500   -0.0060
##    180        2.1775             nan     0.0500   -0.0142
##    200        2.0699             nan     0.0500   -0.0169
##    220        1.9702             nan     0.0500   -0.0133
##    240        1.8870             nan     0.0500   -0.0101
##    260        1.7939             nan     0.0500   -0.0079
##    280        1.7193             nan     0.0500   -0.0083
##    300        1.6625             nan     0.0500   -0.0082
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.3161             nan     0.1000    7.5967
##      2       48.5321             nan     0.1000    5.9673
##      3       43.3385             nan     0.1000    4.9340
##      4       38.8289             nan     0.1000    4.1032
##      5       34.7630             nan     0.1000    3.8697
##      6       31.3677             nan     0.1000    3.5022
##      7       28.3541             nan     0.1000    2.5892
##      8       25.8490             nan     0.1000    2.3528
##      9       23.3261             nan     0.1000    1.9713
##     10       21.4953             nan     0.1000    1.7198
##     20       11.0330             nan     0.1000    0.5097
##     40        5.4589             nan     0.1000    0.1066
##     60        4.1032             nan     0.1000    0.0118
##     80        3.8045             nan     0.1000    0.0068
##    100        3.6322             nan     0.1000   -0.0227
##    120        3.4930             nan     0.1000   -0.0226
##    140        3.3736             nan     0.1000   -0.0234
##    160        3.3006             nan     0.1000   -0.0176
##    180        3.2299             nan     0.1000   -0.0281
##    200        3.1753             nan     0.1000   -0.0066
##    220        3.1126             nan     0.1000   -0.0165
##    240        3.0482             nan     0.1000   -0.0135
##    260        2.9993             nan     0.1000   -0.0278
##    280        2.9428             nan     0.1000   -0.0157
##    300        2.8895             nan     0.1000   -0.0200
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.8221             nan     0.1000    7.5962
##      2       48.1017             nan     0.1000    6.2012
##      3       42.4392             nan     0.1000    5.1381
##      4       38.6368             nan     0.1000    3.6170
##      5       34.6672             nan     0.1000    3.9919
##      6       31.3575             nan     0.1000    2.6233
##      7       28.5477             nan     0.1000    2.7536
##      8       26.2321             nan     0.1000    2.1725
##      9       23.9798             nan     0.1000    2.2318
##     10       22.0617             nan     0.1000    1.8792
##     20       11.0964             nan     0.1000    0.5872
##     40        5.3627             nan     0.1000    0.0595
##     60        4.1855             nan     0.1000    0.0325
##     80        3.8638             nan     0.1000   -0.0056
##    100        3.6775             nan     0.1000   -0.0361
##    120        3.5706             nan     0.1000   -0.0058
##    140        3.4825             nan     0.1000   -0.0022
##    160        3.4043             nan     0.1000   -0.0162
##    180        3.3384             nan     0.1000   -0.0236
##    200        3.2481             nan     0.1000   -0.0083
##    220        3.1899             nan     0.1000   -0.0293
##    240        3.1343             nan     0.1000   -0.0229
##    260        3.0677             nan     0.1000   -0.0037
##    280        3.0078             nan     0.1000   -0.0093
##    300        2.9503             nan     0.1000   -0.0101
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.6917             nan     0.1000    7.4753
##      2       47.9111             nan     0.1000    6.1290
##      3       42.9440             nan     0.1000    4.9682
##      4       38.6625             nan     0.1000    4.4414
##      5       34.9776             nan     0.1000    3.7037
##      6       31.3760             nan     0.1000    3.4863
##      7       28.5369             nan     0.1000    3.1025
##      8       26.3977             nan     0.1000    1.7627
##      9       23.8295             nan     0.1000    2.4272
##     10       21.8973             nan     0.1000    1.9409
##     20       10.9792             nan     0.1000    0.4816
##     40        5.5861             nan     0.1000    0.0929
##     60        4.4016             nan     0.1000    0.0188
##     80        4.0705             nan     0.1000   -0.0102
##    100        3.9154             nan     0.1000   -0.0088
##    120        3.7406             nan     0.1000   -0.0130
##    140        3.6276             nan     0.1000   -0.0231
##    160        3.5139             nan     0.1000   -0.0102
##    180        3.4294             nan     0.1000   -0.0058
##    200        3.3450             nan     0.1000   -0.0184
##    220        3.2711             nan     0.1000   -0.0101
##    240        3.2038             nan     0.1000   -0.0085
##    260        3.1537             nan     0.1000   -0.0276
##    280        3.0923             nan     0.1000   -0.0144
##    300        3.0416             nan     0.1000   -0.0053
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.4726             nan     0.1000    8.9648
##      2       43.8795             nan     0.1000    7.7489
##      3       37.5948             nan     0.1000    6.4576
##      4       32.3294             nan     0.1000    4.7621
##      5       27.5584             nan     0.1000    4.6246
##      6       23.9344             nan     0.1000    3.6162
##      7       20.8036             nan     0.1000    2.9801
##      8       18.0210             nan     0.1000    2.4732
##      9       15.6881             nan     0.1000    2.0917
##     10       14.1115             nan     0.1000    1.4837
##     20        5.7824             nan     0.1000    0.2826
##     40        3.1788             nan     0.1000   -0.0073
##     60        2.6056             nan     0.1000    0.0038
##     80        2.2704             nan     0.1000   -0.0222
##    100        2.0300             nan     0.1000   -0.0106
##    120        1.7811             nan     0.1000   -0.0285
##    140        1.6086             nan     0.1000   -0.0210
##    160        1.4618             nan     0.1000   -0.0226
##    180        1.3599             nan     0.1000   -0.0113
##    200        1.2608             nan     0.1000   -0.0064
##    220        1.1921             nan     0.1000   -0.0145
##    240        1.1012             nan     0.1000   -0.0119
##    260        1.0217             nan     0.1000   -0.0081
##    280        0.9546             nan     0.1000   -0.0104
##    300        0.8919             nan     0.1000   -0.0093
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.2399             nan     0.1000   10.0455
##      2       43.9704             nan     0.1000    7.9486
##      3       38.2288             nan     0.1000    5.2999
##      4       32.3777             nan     0.1000    5.3854
##      5       27.5554             nan     0.1000    4.6569
##      6       23.9197             nan     0.1000    3.8108
##      7       20.9706             nan     0.1000    2.7298
##      8       18.4633             nan     0.1000    2.4062
##      9       15.9500             nan     0.1000    2.1532
##     10       14.1225             nan     0.1000    1.6851
##     20        5.7424             nan     0.1000    0.4124
##     40        3.2993             nan     0.1000    0.0136
##     60        2.7642             nan     0.1000   -0.0247
##     80        2.4472             nan     0.1000   -0.0236
##    100        2.2731             nan     0.1000   -0.0373
##    120        2.1058             nan     0.1000   -0.0137
##    140        1.9387             nan     0.1000   -0.0344
##    160        1.8227             nan     0.1000   -0.0310
##    180        1.6896             nan     0.1000   -0.0236
##    200        1.5886             nan     0.1000   -0.0240
##    220        1.5046             nan     0.1000   -0.0166
##    240        1.4342             nan     0.1000   -0.0292
##    260        1.3655             nan     0.1000   -0.0166
##    280        1.3094             nan     0.1000   -0.0106
##    300        1.2528             nan     0.1000   -0.0109
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.1985             nan     0.1000    8.9546
##      2       44.2912             nan     0.1000    7.8004
##      3       37.7013             nan     0.1000    6.5905
##      4       32.1139             nan     0.1000    4.7618
##      5       27.5704             nan     0.1000    3.8246
##      6       23.8799             nan     0.1000    3.6257
##      7       20.7950             nan     0.1000    3.0542
##      8       18.0271             nan     0.1000    2.5835
##      9       15.8764             nan     0.1000    1.9822
##     10       14.1450             nan     0.1000    1.6344
##     20        6.0919             nan     0.1000    0.2129
##     40        3.5881             nan     0.1000   -0.0376
##     60        3.1061             nan     0.1000   -0.0077
##     80        2.7711             nan     0.1000   -0.0325
##    100        2.5421             nan     0.1000   -0.0315
##    120        2.3426             nan     0.1000   -0.0529
##    140        2.1926             nan     0.1000   -0.0435
##    160        2.0785             nan     0.1000   -0.0148
##    180        1.9620             nan     0.1000   -0.0220
##    200        1.8454             nan     0.1000   -0.0152
##    220        1.7456             nan     0.1000   -0.0089
##    240        1.6746             nan     0.1000   -0.0196
##    260        1.6048             nan     0.1000   -0.0261
##    280        1.5287             nan     0.1000   -0.0253
##    300        1.4665             nan     0.1000   -0.0095
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.7145             nan     0.1000   10.3756
##      2       43.0919             nan     0.1000    8.2583
##      3       36.2916             nan     0.1000    6.1223
##      4       30.4362             nan     0.1000    5.4657
##      5       26.1032             nan     0.1000    5.0175
##      6       22.2192             nan     0.1000    4.0364
##      7       19.0044             nan     0.1000    2.7087
##      8       16.5323             nan     0.1000    2.2295
##      9       14.4749             nan     0.1000    1.7116
##     10       12.6749             nan     0.1000    1.8570
##     20        4.8160             nan     0.1000    0.2611
##     40        2.5902             nan     0.1000   -0.0003
##     60        2.0046             nan     0.1000   -0.0276
##     80        1.6307             nan     0.1000   -0.0076
##    100        1.4037             nan     0.1000   -0.0251
##    120        1.2264             nan     0.1000   -0.0189
##    140        1.0591             nan     0.1000   -0.0151
##    160        0.9383             nan     0.1000   -0.0214
##    180        0.8296             nan     0.1000   -0.0079
##    200        0.7226             nan     0.1000   -0.0169
##    220        0.6533             nan     0.1000   -0.0094
##    240        0.5826             nan     0.1000   -0.0124
##    260        0.5241             nan     0.1000   -0.0110
##    280        0.4698             nan     0.1000   -0.0047
##    300        0.4292             nan     0.1000   -0.0101
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.6871             nan     0.1000    9.4432
##      2       43.1760             nan     0.1000    8.5284
##      3       36.2803             nan     0.1000    7.2351
##      4       30.6930             nan     0.1000    5.7188
##      5       26.0779             nan     0.1000    4.5128
##      6       22.0238             nan     0.1000    3.9601
##      7       19.0725             nan     0.1000    2.9886
##      8       16.4103             nan     0.1000    2.5538
##      9       14.3840             nan     0.1000    1.8303
##     10       12.4843             nan     0.1000    1.7952
##     20        4.8169             nan     0.1000    0.2098
##     40        2.7760             nan     0.1000    0.0002
##     60        2.2630             nan     0.1000   -0.0166
##     80        1.9706             nan     0.1000   -0.0431
##    100        1.7534             nan     0.1000   -0.0199
##    120        1.5730             nan     0.1000   -0.0319
##    140        1.4006             nan     0.1000   -0.0157
##    160        1.2674             nan     0.1000   -0.0187
##    180        1.1430             nan     0.1000   -0.0192
##    200        1.0472             nan     0.1000   -0.0167
##    220        0.9740             nan     0.1000   -0.0158
##    240        0.8996             nan     0.1000   -0.0077
##    260        0.8109             nan     0.1000   -0.0153
##    280        0.7482             nan     0.1000   -0.0095
##    300        0.7035             nan     0.1000   -0.0123
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.5345             nan     0.1000   10.3054
##      2       43.0925             nan     0.1000    8.4375
##      3       36.0068             nan     0.1000    7.2820
##      4       30.4364             nan     0.1000    5.7147
##      5       25.4884             nan     0.1000    4.8591
##      6       21.7094             nan     0.1000    3.4260
##      7       18.6532             nan     0.1000    3.0285
##      8       16.0543             nan     0.1000    2.5504
##      9       14.1676             nan     0.1000    1.7930
##     10       12.3412             nan     0.1000    1.6605
##     20        5.0075             nan     0.1000    0.2380
##     40        3.1119             nan     0.1000   -0.0072
##     60        2.6684             nan     0.1000   -0.0126
##     80        2.4027             nan     0.1000   -0.0623
##    100        2.1318             nan     0.1000   -0.0220
##    120        1.9685             nan     0.1000   -0.0340
##    140        1.8308             nan     0.1000   -0.0214
##    160        1.6708             nan     0.1000   -0.0243
##    180        1.5475             nan     0.1000   -0.0271
##    200        1.4144             nan     0.1000   -0.0108
##    220        1.3058             nan     0.1000   -0.0215
##    240        1.2154             nan     0.1000   -0.0254
##    260        1.1360             nan     0.1000   -0.0090
##    280        1.0795             nan     0.1000   -0.0114
##    300        1.0089             nan     0.1000   -0.0138
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.2146             nan     0.0100    0.7571
##      2       60.4418             nan     0.0100    0.7667
##      3       59.6427             nan     0.0100    0.7732
##      4       58.8563             nan     0.0100    0.7111
##      5       58.1218             nan     0.0100    0.7164
##      6       57.3844             nan     0.0100    0.7175
##      7       56.6627             nan     0.0100    0.7603
##      8       55.9861             nan     0.0100    0.6582
##      9       55.3267             nan     0.0100    0.6970
##     10       54.6633             nan     0.0100    0.6140
##     20       48.7058             nan     0.0100    0.4878
##     40       39.0761             nan     0.0100    0.3942
##     60       31.8254             nan     0.0100    0.3262
##     80       26.2326             nan     0.0100    0.2038
##    100       22.0595             nan     0.0100    0.1801
##    120       18.8423             nan     0.0100    0.0819
##    140       16.3048             nan     0.0100    0.0959
##    160       14.1778             nan     0.0100    0.0775
##    180       12.5247             nan     0.0100    0.0552
##    200       11.1925             nan     0.0100    0.0533
##    220       10.0811             nan     0.0100    0.0429
##    240        9.1473             nan     0.0100    0.0298
##    260        8.3516             nan     0.0100    0.0244
##    280        7.6500             nan     0.0100    0.0266
##    300        7.0593             nan     0.0100    0.0240
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.2101             nan     0.0100    0.7058
##      2       60.4066             nan     0.0100    0.7338
##      3       59.6159             nan     0.0100    0.7538
##      4       58.9031             nan     0.0100    0.7667
##      5       58.2160             nan     0.0100    0.6699
##      6       57.4825             nan     0.0100    0.7249
##      7       56.7370             nan     0.0100    0.7177
##      8       56.0350             nan     0.0100    0.6768
##      9       55.3693             nan     0.0100    0.6461
##     10       54.7009             nan     0.0100    0.6582
##     20       48.5406             nan     0.0100    0.5734
##     40       39.1154             nan     0.0100    0.4282
##     60       31.9170             nan     0.0100    0.3021
##     80       26.4411             nan     0.0100    0.2322
##    100       22.1562             nan     0.0100    0.1720
##    120       18.9394             nan     0.0100    0.1307
##    140       16.2945             nan     0.0100    0.0892
##    160       14.2501             nan     0.0100    0.0950
##    180       12.6227             nan     0.0100    0.0704
##    200       11.2290             nan     0.0100    0.0646
##    220       10.1104             nan     0.0100    0.0350
##    240        9.1508             nan     0.0100    0.0344
##    260        8.3164             nan     0.0100    0.0368
##    280        7.6501             nan     0.0100    0.0299
##    300        7.0750             nan     0.0100    0.0142
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.2331             nan     0.0100    0.7881
##      2       60.4461             nan     0.0100    0.7510
##      3       59.6411             nan     0.0100    0.6633
##      4       58.8597             nan     0.0100    0.7500
##      5       58.1200             nan     0.0100    0.7374
##      6       57.4290             nan     0.0100    0.6743
##      7       56.7738             nan     0.0100    0.6543
##      8       56.1146             nan     0.0100    0.6179
##      9       55.4434             nan     0.0100    0.6502
##     10       54.7779             nan     0.0100    0.6477
##     20       48.7296             nan     0.0100    0.5429
##     40       39.1938             nan     0.0100    0.3492
##     60       31.7242             nan     0.0100    0.2906
##     80       26.2641             nan     0.0100    0.1913
##    100       22.0691             nan     0.0100    0.1638
##    120       18.7761             nan     0.0100    0.1298
##    140       16.2797             nan     0.0100    0.0982
##    160       14.2441             nan     0.0100    0.0761
##    180       12.5985             nan     0.0100    0.0769
##    200       11.2723             nan     0.0100    0.0487
##    220       10.1543             nan     0.0100    0.0372
##    240        9.1683             nan     0.0100    0.0372
##    260        8.3710             nan     0.0100    0.0340
##    280        7.7122             nan     0.0100    0.0182
##    300        7.1286             nan     0.0100    0.0171
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.9207             nan     0.0100    1.0617
##      2       59.9500             nan     0.0100    0.9774
##      3       58.9181             nan     0.0100    1.0423
##      4       57.9789             nan     0.0100    0.9182
##      5       57.0590             nan     0.0100    1.0044
##      6       56.1512             nan     0.0100    0.9246
##      7       55.1854             nan     0.0100    0.9386
##      8       54.2744             nan     0.0100    0.9058
##      9       53.4214             nan     0.0100    0.8462
##     10       52.5447             nan     0.0100    0.8344
##     20       44.5667             nan     0.0100    0.6811
##     40       32.5573             nan     0.0100    0.5254
##     60       24.2591             nan     0.0100    0.3446
##     80       18.4772             nan     0.0100    0.2190
##    100       14.3530             nan     0.0100    0.1786
##    120       11.3916             nan     0.0100    0.1027
##    140        9.3115             nan     0.0100    0.0759
##    160        7.7075             nan     0.0100    0.0542
##    180        6.5679             nan     0.0100    0.0291
##    200        5.6977             nan     0.0100    0.0229
##    220        5.0259             nan     0.0100    0.0228
##    240        4.5150             nan     0.0100    0.0219
##    260        4.1228             nan     0.0100    0.0128
##    280        3.8013             nan     0.0100    0.0099
##    300        3.5468             nan     0.0100    0.0063
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.9683             nan     0.0100    0.8833
##      2       59.9679             nan     0.0100    0.9525
##      3       58.9316             nan     0.0100    0.9552
##      4       57.9438             nan     0.0100    0.9942
##      5       56.9863             nan     0.0100    0.8982
##      6       56.0058             nan     0.0100    0.9342
##      7       55.1156             nan     0.0100    0.9748
##      8       54.2441             nan     0.0100    0.9168
##      9       53.3840             nan     0.0100    0.8944
##     10       52.5425             nan     0.0100    0.8226
##     20       44.7535             nan     0.0100    0.7515
##     40       32.6659             nan     0.0100    0.4574
##     60       24.3050             nan     0.0100    0.3278
##     80       18.5316             nan     0.0100    0.2395
##    100       14.5070             nan     0.0100    0.1561
##    120       11.5341             nan     0.0100    0.1093
##    140        9.4060             nan     0.0100    0.0801
##    160        7.8312             nan     0.0100    0.0603
##    180        6.6948             nan     0.0100    0.0406
##    200        5.8077             nan     0.0100    0.0251
##    220        5.1159             nan     0.0100    0.0230
##    240        4.5981             nan     0.0100    0.0181
##    260        4.2136             nan     0.0100    0.0110
##    280        3.9053             nan     0.0100    0.0112
##    300        3.6720             nan     0.0100    0.0048
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.0170             nan     0.0100    1.0234
##      2       59.9716             nan     0.0100    0.9814
##      3       58.9577             nan     0.0100    1.0491
##      4       57.9841             nan     0.0100    1.0418
##      5       57.0659             nan     0.0100    0.9546
##      6       56.0661             nan     0.0100    0.9480
##      7       55.0854             nan     0.0100    0.9326
##      8       54.1699             nan     0.0100    0.9218
##      9       53.2629             nan     0.0100    1.0079
##     10       52.3588             nan     0.0100    0.8554
##     20       44.5512             nan     0.0100    0.6441
##     40       32.5981             nan     0.0100    0.4712
##     60       24.3222             nan     0.0100    0.3173
##     80       18.6177             nan     0.0100    0.2361
##    100       14.6100             nan     0.0100    0.1648
##    120       11.6843             nan     0.0100    0.1067
##    140        9.5559             nan     0.0100    0.0870
##    160        8.0146             nan     0.0100    0.0602
##    180        6.8107             nan     0.0100    0.0493
##    200        5.9598             nan     0.0100    0.0210
##    220        5.3236             nan     0.0100    0.0226
##    240        4.7931             nan     0.0100    0.0190
##    260        4.4029             nan     0.0100    0.0096
##    280        4.1061             nan     0.0100    0.0102
##    300        3.8722             nan     0.0100    0.0061
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.8599             nan     0.0100    1.1045
##      2       59.7604             nan     0.0100    0.9577
##      3       58.7324             nan     0.0100    1.0759
##      4       57.6728             nan     0.0100    1.0633
##      5       56.6852             nan     0.0100    1.0514
##      6       55.6812             nan     0.0100    0.9947
##      7       54.6859             nan     0.0100    1.0101
##      8       53.7895             nan     0.0100    1.0009
##      9       52.9049             nan     0.0100    0.9661
##     10       52.0180             nan     0.0100    0.8686
##     20       43.7365             nan     0.0100    0.6808
##     40       31.1704             nan     0.0100    0.5059
##     60       22.6080             nan     0.0100    0.3243
##     80       16.7924             nan     0.0100    0.2327
##    100       12.6529             nan     0.0100    0.1739
##    120        9.8255             nan     0.0100    0.1081
##    140        7.8145             nan     0.0100    0.0805
##    160        6.3392             nan     0.0100    0.0546
##    180        5.2905             nan     0.0100    0.0444
##    200        4.5580             nan     0.0100    0.0262
##    220        4.0028             nan     0.0100    0.0108
##    240        3.5816             nan     0.0100    0.0081
##    260        3.2739             nan     0.0100    0.0070
##    280        3.0261             nan     0.0100    0.0037
##    300        2.8510             nan     0.0100   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.8466             nan     0.0100    1.1105
##      2       59.8173             nan     0.0100    1.0447
##      3       58.7741             nan     0.0100    0.9922
##      4       57.8056             nan     0.0100    1.0112
##      5       56.7986             nan     0.0100    0.9769
##      6       55.8752             nan     0.0100    0.9015
##      7       54.8514             nan     0.0100    1.0506
##      8       53.9511             nan     0.0100    0.9219
##      9       52.9724             nan     0.0100    0.9615
##     10       52.0419             nan     0.0100    0.9956
##     20       43.6749             nan     0.0100    0.8140
##     40       31.2287             nan     0.0100    0.5267
##     60       22.5799             nan     0.0100    0.3256
##     80       16.8578             nan     0.0100    0.2478
##    100       12.7729             nan     0.0100    0.1684
##    120        9.9485             nan     0.0100    0.1144
##    140        7.9676             nan     0.0100    0.0683
##    160        6.5100             nan     0.0100    0.0572
##    180        5.4399             nan     0.0100    0.0358
##    200        4.7002             nan     0.0100    0.0276
##    220        4.1695             nan     0.0100    0.0184
##    240        3.7746             nan     0.0100    0.0103
##    260        3.4784             nan     0.0100    0.0007
##    280        3.2470             nan     0.0100    0.0029
##    300        3.0648             nan     0.0100    0.0043
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.9114             nan     0.0100    1.0235
##      2       59.8544             nan     0.0100    1.0829
##      3       58.8256             nan     0.0100    1.0491
##      4       57.8158             nan     0.0100    1.0343
##      5       56.7871             nan     0.0100    1.0397
##      6       55.8776             nan     0.0100    0.9607
##      7       54.8405             nan     0.0100    0.9479
##      8       53.9008             nan     0.0100    1.0173
##      9       52.9865             nan     0.0100    0.8911
##     10       52.0564             nan     0.0100    0.9678
##     20       43.8467             nan     0.0100    0.7355
##     40       31.4376             nan     0.0100    0.5162
##     60       22.9465             nan     0.0100    0.3335
##     80       17.1166             nan     0.0100    0.2320
##    100       13.1458             nan     0.0100    0.1522
##    120       10.3169             nan     0.0100    0.1267
##    140        8.3525             nan     0.0100    0.0663
##    160        6.8938             nan     0.0100    0.0608
##    180        5.8778             nan     0.0100    0.0362
##    200        5.1397             nan     0.0100    0.0247
##    220        4.5938             nan     0.0100    0.0237
##    240        4.1886             nan     0.0100    0.0136
##    260        3.8909             nan     0.0100    0.0114
##    280        3.6706             nan     0.0100    0.0014
##    300        3.4952             nan     0.0100    0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.0320             nan     0.0500    3.9036
##      2       54.5431             nan     0.0500    3.1505
##      3       51.3175             nan     0.0500    3.3125
##      4       48.5021             nan     0.0500    2.9328
##      5       45.5377             nan     0.0500    2.7748
##      6       43.2551             nan     0.0500    2.1519
##      7       41.0764             nan     0.0500    2.0129
##      8       38.9532             nan     0.0500    2.1007
##      9       36.6735             nan     0.0500    1.9303
##     10       34.8733             nan     0.0500    1.7155
##     20       21.8982             nan     0.0500    0.9277
##     40       11.0476             nan     0.0500    0.2596
##     60        6.9322             nan     0.0500    0.0827
##     80        5.0555             nan     0.0500    0.0309
##    100        4.1734             nan     0.0500    0.0137
##    120        3.7203             nan     0.0500    0.0069
##    140        3.4903             nan     0.0500   -0.0055
##    160        3.3660             nan     0.0500   -0.0207
##    180        3.2785             nan     0.0500   -0.0133
##    200        3.2111             nan     0.0500   -0.0064
##    220        3.1337             nan     0.0500   -0.0177
##    240        3.0943             nan     0.0500   -0.0157
##    260        3.0177             nan     0.0500   -0.0039
##    280        2.9654             nan     0.0500   -0.0076
##    300        2.9303             nan     0.0500   -0.0175
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.8246             nan     0.0500    3.8069
##      2       54.5173             nan     0.0500    3.4161
##      3       51.5395             nan     0.0500    3.3087
##      4       48.6179             nan     0.0500    3.0133
##      5       45.9722             nan     0.0500    2.8870
##      6       43.6940             nan     0.0500    2.3218
##      7       41.3385             nan     0.0500    2.2202
##      8       39.2852             nan     0.0500    2.0523
##      9       37.3543             nan     0.0500    1.7968
##     10       35.5395             nan     0.0500    1.7426
##     20       21.9442             nan     0.0500    1.0094
##     40       11.0329             nan     0.0500    0.2508
##     60        6.8113             nan     0.0500    0.1251
##     80        5.0744             nan     0.0500    0.0393
##    100        4.2370             nan     0.0500    0.0108
##    120        3.8250             nan     0.0500    0.0046
##    140        3.6007             nan     0.0500   -0.0011
##    160        3.4859             nan     0.0500   -0.0121
##    180        3.3932             nan     0.0500   -0.0091
##    200        3.3100             nan     0.0500   -0.0063
##    220        3.2454             nan     0.0500    0.0011
##    240        3.1762             nan     0.0500   -0.0037
##    260        3.1160             nan     0.0500   -0.0041
##    280        3.0701             nan     0.0500   -0.0065
##    300        3.0351             nan     0.0500   -0.0096
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.0441             nan     0.0500    3.7926
##      2       54.8287             nan     0.0500    3.6888
##      3       51.5855             nan     0.0500    3.0969
##      4       48.2923             nan     0.0500    2.7331
##      5       45.6297             nan     0.0500    2.7493
##      6       43.1559             nan     0.0500    2.5933
##      7       40.9070             nan     0.0500    2.1149
##      8       38.5866             nan     0.0500    1.9367
##      9       36.6783             nan     0.0500    1.9797
##     10       35.0153             nan     0.0500    1.7954
##     20       22.1595             nan     0.0500    0.9428
##     40       11.1369             nan     0.0500    0.2178
##     60        7.1095             nan     0.0500    0.1208
##     80        5.2042             nan     0.0500    0.0616
##    100        4.3460             nan     0.0500    0.0219
##    120        3.9957             nan     0.0500   -0.0275
##    140        3.8210             nan     0.0500   -0.0185
##    160        3.6894             nan     0.0500   -0.0012
##    180        3.5919             nan     0.0500   -0.0037
##    200        3.5230             nan     0.0500   -0.0092
##    220        3.4410             nan     0.0500   -0.0068
##    240        3.3806             nan     0.0500   -0.0187
##    260        3.3166             nan     0.0500   -0.0033
##    280        3.2670             nan     0.0500   -0.0127
##    300        3.2187             nan     0.0500   -0.0120
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.9732             nan     0.0500    5.0766
##      2       52.4679             nan     0.0500    4.3311
##      3       48.3264             nan     0.0500    3.8133
##      4       44.4243             nan     0.0500    3.6729
##      5       40.9426             nan     0.0500    3.7253
##      6       37.8368             nan     0.0500    3.1277
##      7       34.8356             nan     0.0500    2.7551
##      8       32.2039             nan     0.0500    2.4053
##      9       29.7965             nan     0.0500    2.1979
##     10       27.6176             nan     0.0500    2.0831
##     20       14.1842             nan     0.0500    0.8042
##     40        5.7740             nan     0.0500    0.1591
##     60        3.5590             nan     0.0500    0.0044
##     80        2.9460             nan     0.0500   -0.0268
##    100        2.6220             nan     0.0500   -0.0039
##    120        2.3881             nan     0.0500   -0.0067
##    140        2.2174             nan     0.0500   -0.0256
##    160        2.0517             nan     0.0500   -0.0223
##    180        1.9344             nan     0.0500   -0.0076
##    200        1.8351             nan     0.0500   -0.0169
##    220        1.7387             nan     0.0500   -0.0091
##    240        1.6488             nan     0.0500   -0.0120
##    260        1.5618             nan     0.0500   -0.0094
##    280        1.4899             nan     0.0500   -0.0045
##    300        1.4252             nan     0.0500   -0.0073
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.7626             nan     0.0500    5.2616
##      2       52.2961             nan     0.0500    4.2161
##      3       48.1645             nan     0.0500    3.6381
##      4       44.2978             nan     0.0500    4.0835
##      5       40.6974             nan     0.0500    3.7189
##      6       37.5823             nan     0.0500    3.0162
##      7       34.7504             nan     0.0500    2.7777
##      8       32.1139             nan     0.0500    2.5326
##      9       29.6980             nan     0.0500    2.4592
##     10       27.4756             nan     0.0500    2.0332
##     20       14.0349             nan     0.0500    0.8806
##     40        5.5720             nan     0.0500    0.1319
##     60        3.5739             nan     0.0500    0.0133
##     80        3.0852             nan     0.0500   -0.0028
##    100        2.7889             nan     0.0500   -0.0090
##    120        2.5724             nan     0.0500   -0.0073
##    140        2.4002             nan     0.0500   -0.0085
##    160        2.2654             nan     0.0500   -0.0123
##    180        2.1400             nan     0.0500   -0.0147
##    200        2.0371             nan     0.0500   -0.0063
##    220        1.9388             nan     0.0500   -0.0162
##    240        1.8495             nan     0.0500   -0.0126
##    260        1.7852             nan     0.0500   -0.0208
##    280        1.7209             nan     0.0500   -0.0100
##    300        1.6538             nan     0.0500   -0.0078
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.9713             nan     0.0500    5.4928
##      2       52.4291             nan     0.0500    4.4699
##      3       48.3727             nan     0.0500    4.0367
##      4       44.4707             nan     0.0500    3.8570
##      5       40.9740             nan     0.0500    3.6530
##      6       37.9045             nan     0.0500    2.9720
##      7       34.8866             nan     0.0500    3.2349
##      8       32.2309             nan     0.0500    2.5453
##      9       29.7234             nan     0.0500    2.0863
##     10       27.7302             nan     0.0500    1.9942
##     20       14.3717             nan     0.0500    0.8875
##     40        5.9060             nan     0.0500    0.1763
##     60        3.9694             nan     0.0500    0.0145
##     80        3.4253             nan     0.0500   -0.0133
##    100        3.1380             nan     0.0500   -0.0205
##    120        2.9462             nan     0.0500   -0.0122
##    140        2.7932             nan     0.0500   -0.0273
##    160        2.6559             nan     0.0500   -0.0077
##    180        2.5309             nan     0.0500   -0.0160
##    200        2.4088             nan     0.0500   -0.0144
##    220        2.3305             nan     0.0500   -0.0130
##    240        2.2463             nan     0.0500   -0.0256
##    260        2.1812             nan     0.0500   -0.0085
##    280        2.0929             nan     0.0500   -0.0079
##    300        2.0246             nan     0.0500   -0.0124
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.6527             nan     0.0500    5.4209
##      2       51.3889             nan     0.0500    5.3058
##      3       46.8690             nan     0.0500    4.0886
##      4       42.9162             nan     0.0500    3.8024
##      5       39.3671             nan     0.0500    3.4322
##      6       36.2320             nan     0.0500    3.2942
##      7       33.1884             nan     0.0500    3.0409
##      8       30.5938             nan     0.0500    2.5090
##      9       28.1127             nan     0.0500    2.5185
##     10       25.9688             nan     0.0500    2.2446
##     20       12.3933             nan     0.0500    0.7846
##     40        4.4251             nan     0.0500    0.1171
##     60        2.7606             nan     0.0500   -0.0043
##     80        2.2441             nan     0.0500   -0.0061
##    100        1.9671             nan     0.0500   -0.0125
##    120        1.7889             nan     0.0500   -0.0104
##    140        1.6247             nan     0.0500   -0.0122
##    160        1.4880             nan     0.0500   -0.0014
##    180        1.3664             nan     0.0500   -0.0096
##    200        1.2483             nan     0.0500   -0.0045
##    220        1.1622             nan     0.0500   -0.0060
##    240        1.0733             nan     0.0500   -0.0074
##    260        0.9910             nan     0.0500   -0.0068
##    280        0.9281             nan     0.0500   -0.0112
##    300        0.8563             nan     0.0500   -0.0029
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.7463             nan     0.0500    5.5604
##      2       51.8746             nan     0.0500    4.5910
##      3       47.3341             nan     0.0500    4.7281
##      4       43.3017             nan     0.0500    4.4060
##      5       39.7371             nan     0.0500    3.3006
##      6       36.7604             nan     0.0500    3.1659
##      7       33.6199             nan     0.0500    2.5288
##      8       30.9942             nan     0.0500    2.6074
##      9       28.5158             nan     0.0500    2.3772
##     10       26.3997             nan     0.0500    2.1882
##     20       12.6284             nan     0.0500    0.8531
##     40        4.6844             nan     0.0500    0.1644
##     60        3.0655             nan     0.0500    0.0135
##     80        2.5299             nan     0.0500   -0.0102
##    100        2.2663             nan     0.0500   -0.0152
##    120        2.0987             nan     0.0500   -0.0134
##    140        1.9400             nan     0.0500   -0.0008
##    160        1.8088             nan     0.0500   -0.0139
##    180        1.7125             nan     0.0500   -0.0149
##    200        1.5990             nan     0.0500   -0.0219
##    220        1.4907             nan     0.0500   -0.0180
##    240        1.4066             nan     0.0500   -0.0061
##    260        1.3280             nan     0.0500   -0.0065
##    280        1.2644             nan     0.0500   -0.0080
##    300        1.1963             nan     0.0500   -0.0154
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.4938             nan     0.0500    5.2998
##      2       51.8435             nan     0.0500    4.9099
##      3       47.4414             nan     0.0500    3.9309
##      4       43.3504             nan     0.0500    4.2734
##      5       39.7379             nan     0.0500    3.3469
##      6       36.7073             nan     0.0500    3.2462
##      7       33.6378             nan     0.0500    2.9720
##      8       30.9603             nan     0.0500    2.8667
##      9       28.5738             nan     0.0500    2.4097
##     10       26.2968             nan     0.0500    2.2067
##     20       12.8494             nan     0.0500    0.9390
##     40        4.9731             nan     0.0500    0.0948
##     60        3.4319             nan     0.0500    0.0035
##     80        2.9577             nan     0.0500   -0.0232
##    100        2.7100             nan     0.0500   -0.0110
##    120        2.5209             nan     0.0500   -0.0142
##    140        2.3386             nan     0.0500   -0.0088
##    160        2.2338             nan     0.0500   -0.0102
##    180        2.1042             nan     0.0500   -0.0194
##    200        1.9966             nan     0.0500   -0.0109
##    220        1.8962             nan     0.0500   -0.0032
##    240        1.8105             nan     0.0500   -0.0221
##    260        1.7334             nan     0.0500   -0.0075
##    280        1.6411             nan     0.0500   -0.0117
##    300        1.5873             nan     0.0500   -0.0104
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.1977             nan     0.1000    7.4660
##      2       48.2178             nan     0.1000    5.9712
##      3       43.1700             nan     0.1000    4.1692
##      4       38.3877             nan     0.1000    4.4018
##      5       33.9463             nan     0.1000    3.7847
##      6       30.6617             nan     0.1000    2.9239
##      7       27.9505             nan     0.1000    2.6399
##      8       25.3583             nan     0.1000    2.8621
##      9       22.9037             nan     0.1000    2.1542
##     10       20.8359             nan     0.1000    1.9039
##     20       11.0221             nan     0.1000    0.7067
##     40        5.0339             nan     0.1000    0.0895
##     60        3.7857             nan     0.1000    0.0198
##     80        3.4479             nan     0.1000   -0.0242
##    100        3.2798             nan     0.1000   -0.0107
##    120        3.1685             nan     0.1000   -0.0306
##    140        3.0644             nan     0.1000   -0.0347
##    160        2.9826             nan     0.1000   -0.0131
##    180        2.9169             nan     0.1000   -0.0179
##    200        2.8581             nan     0.1000   -0.0097
##    220        2.7914             nan     0.1000   -0.0086
##    240        2.7264             nan     0.1000   -0.0089
##    260        2.6905             nan     0.1000   -0.0141
##    280        2.6638             nan     0.1000   -0.0289
##    300        2.5957             nan     0.1000   -0.0248
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.3415             nan     0.1000    7.1067
##      2       48.5181             nan     0.1000    6.2958
##      3       43.2629             nan     0.1000    5.1808
##      4       38.8502             nan     0.1000    4.2454
##      5       35.0131             nan     0.1000    3.6627
##      6       31.6117             nan     0.1000    3.3416
##      7       28.3329             nan     0.1000    2.9352
##      8       25.5805             nan     0.1000    2.5954
##      9       23.3408             nan     0.1000    1.9313
##     10       21.5298             nan     0.1000    1.7782
##     20       10.9913             nan     0.1000    0.4158
##     40        5.1504             nan     0.1000    0.1276
##     60        3.9973             nan     0.1000    0.0090
##     80        3.6917             nan     0.1000   -0.0281
##    100        3.5124             nan     0.1000   -0.0038
##    120        3.3691             nan     0.1000   -0.0213
##    140        3.2594             nan     0.1000   -0.0156
##    160        3.1812             nan     0.1000   -0.0512
##    180        3.0930             nan     0.1000   -0.0121
##    200        3.0201             nan     0.1000   -0.0090
##    220        2.9672             nan     0.1000   -0.0123
##    240        2.9189             nan     0.1000   -0.0117
##    260        2.8583             nan     0.1000   -0.0131
##    280        2.8068             nan     0.1000   -0.0132
##    300        2.7506             nan     0.1000   -0.0132
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.5799             nan     0.1000    7.5566
##      2       48.5670             nan     0.1000    6.2876
##      3       43.3600             nan     0.1000    5.2236
##      4       38.5741             nan     0.1000    4.5952
##      5       34.6908             nan     0.1000    3.5599
##      6       31.3011             nan     0.1000    3.0332
##      7       28.4832             nan     0.1000    2.7306
##      8       25.6532             nan     0.1000    2.6216
##      9       23.4697             nan     0.1000    2.0832
##     10       21.4403             nan     0.1000    1.6425
##     20       11.0070             nan     0.1000    0.4945
##     40        5.2678             nan     0.1000    0.0732
##     60        4.0230             nan     0.1000    0.0062
##     80        3.6998             nan     0.1000   -0.0283
##    100        3.5201             nan     0.1000   -0.0098
##    120        3.3967             nan     0.1000   -0.0081
##    140        3.2490             nan     0.1000   -0.0474
##    160        3.1703             nan     0.1000   -0.0180
##    180        3.1149             nan     0.1000   -0.0271
##    200        3.0444             nan     0.1000   -0.0383
##    220        2.9657             nan     0.1000   -0.0251
##    240        2.8947             nan     0.1000   -0.0369
##    260        2.8454             nan     0.1000   -0.0090
##    280        2.8107             nan     0.1000   -0.0119
##    300        2.7774             nan     0.1000   -0.0130
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.1763             nan     0.1000   10.3046
##      2       44.2383             nan     0.1000    8.2328
##      3       37.3153             nan     0.1000    6.3583
##      4       31.9485             nan     0.1000    5.5184
##      5       27.4657             nan     0.1000    3.9867
##      6       23.3813             nan     0.1000    3.5071
##      7       20.1588             nan     0.1000    3.0212
##      8       17.6364             nan     0.1000    2.6216
##      9       15.5000             nan     0.1000    2.0555
##     10       13.6467             nan     0.1000    1.5339
##     20        5.3586             nan     0.1000    0.3444
##     40        2.9241             nan     0.1000   -0.0112
##     60        2.3350             nan     0.1000   -0.0110
##     80        2.0820             nan     0.1000   -0.0170
##    100        1.8367             nan     0.1000   -0.0142
##    120        1.6791             nan     0.1000   -0.0313
##    140        1.5618             nan     0.1000   -0.0356
##    160        1.4122             nan     0.1000   -0.0147
##    180        1.3137             nan     0.1000   -0.0235
##    200        1.2206             nan     0.1000   -0.0130
##    220        1.1456             nan     0.1000   -0.0122
##    240        1.0858             nan     0.1000   -0.0128
##    260        1.0233             nan     0.1000   -0.0046
##    280        0.9600             nan     0.1000   -0.0083
##    300        0.8859             nan     0.1000   -0.0042
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.3655             nan     0.1000    9.5434
##      2       44.3622             nan     0.1000    7.3696
##      3       38.0453             nan     0.1000    5.7417
##      4       32.6289             nan     0.1000    6.1526
##      5       27.6899             nan     0.1000    4.2952
##      6       23.8558             nan     0.1000    3.8494
##      7       20.4550             nan     0.1000    3.5154
##      8       17.9055             nan     0.1000    2.5476
##      9       15.6375             nan     0.1000    2.0716
##     10       13.9656             nan     0.1000    1.6323
##     20        5.5211             nan     0.1000    0.2734
##     40        3.2364             nan     0.1000   -0.0001
##     60        2.6540             nan     0.1000    0.0034
##     80        2.3800             nan     0.1000   -0.0281
##    100        2.1544             nan     0.1000   -0.0049
##    120        1.9728             nan     0.1000   -0.0159
##    140        1.8236             nan     0.1000   -0.0473
##    160        1.7040             nan     0.1000   -0.0176
##    180        1.6045             nan     0.1000   -0.0202
##    200        1.4994             nan     0.1000   -0.0253
##    220        1.3982             nan     0.1000   -0.0162
##    240        1.3133             nan     0.1000   -0.0178
##    260        1.2456             nan     0.1000   -0.0111
##    280        1.1974             nan     0.1000   -0.0143
##    300        1.1411             nan     0.1000   -0.0130
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.4274             nan     0.1000   10.1398
##      2       44.4224             nan     0.1000    8.2069
##      3       38.0346             nan     0.1000    6.8476
##      4       32.5190             nan     0.1000    4.8989
##      5       27.8687             nan     0.1000    3.9048
##      6       24.2671             nan     0.1000    3.4605
##      7       20.8978             nan     0.1000    3.1609
##      8       18.2995             nan     0.1000    2.5340
##      9       16.2640             nan     0.1000    1.8258
##     10       14.3728             nan     0.1000    1.8466
##     20        5.8116             nan     0.1000    0.2398
##     40        3.4061             nan     0.1000   -0.0041
##     60        2.9419             nan     0.1000   -0.0363
##     80        2.6451             nan     0.1000   -0.0209
##    100        2.4001             nan     0.1000   -0.0230
##    120        2.2060             nan     0.1000   -0.0294
##    140        2.0125             nan     0.1000   -0.0084
##    160        1.8804             nan     0.1000   -0.0183
##    180        1.7790             nan     0.1000   -0.0200
##    200        1.6883             nan     0.1000   -0.0227
##    220        1.5905             nan     0.1000   -0.0286
##    240        1.5090             nan     0.1000   -0.0141
##    260        1.4484             nan     0.1000   -0.0111
##    280        1.3860             nan     0.1000   -0.0195
##    300        1.3285             nan     0.1000   -0.0124
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.6338             nan     0.1000   10.6310
##      2       43.1409             nan     0.1000    7.8526
##      3       35.8119             nan     0.1000    6.9085
##      4       30.2242             nan     0.1000    5.5063
##      5       25.1533             nan     0.1000    5.0691
##      6       21.3656             nan     0.1000    3.7887
##      7       18.2957             nan     0.1000    2.9007
##      8       15.7961             nan     0.1000    2.3271
##      9       13.6561             nan     0.1000    2.1348
##     10       11.8394             nan     0.1000    1.6803
##     20        4.3106             nan     0.1000    0.2317
##     40        2.3415             nan     0.1000   -0.0308
##     60        1.8198             nan     0.1000   -0.0168
##     80        1.5360             nan     0.1000   -0.0123
##    100        1.2859             nan     0.1000   -0.0163
##    120        1.0852             nan     0.1000   -0.0188
##    140        0.9553             nan     0.1000   -0.0140
##    160        0.8472             nan     0.1000   -0.0142
##    180        0.7443             nan     0.1000   -0.0144
##    200        0.6633             nan     0.1000   -0.0096
##    220        0.5865             nan     0.1000   -0.0075
##    240        0.5197             nan     0.1000   -0.0067
##    260        0.4666             nan     0.1000   -0.0147
##    280        0.4194             nan     0.1000   -0.0104
##    300        0.3754             nan     0.1000   -0.0051
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.2678             nan     0.1000   10.9446
##      2       42.2272             nan     0.1000    9.5996
##      3       35.6808             nan     0.1000    6.7896
##      4       30.1885             nan     0.1000    5.5910
##      5       25.5106             nan     0.1000    4.6908
##      6       21.6233             nan     0.1000    4.2761
##      7       18.3163             nan     0.1000    3.0600
##      8       15.5537             nan     0.1000    2.3474
##      9       13.7931             nan     0.1000    1.9919
##     10       12.1228             nan     0.1000    1.6952
##     20        4.6926             nan     0.1000    0.1830
##     40        2.7273             nan     0.1000   -0.0374
##     60        2.2169             nan     0.1000   -0.0309
##     80        1.9304             nan     0.1000   -0.0521
##    100        1.7005             nan     0.1000   -0.0588
##    120        1.5040             nan     0.1000   -0.0177
##    140        1.3514             nan     0.1000   -0.0204
##    160        1.2131             nan     0.1000   -0.0137
##    180        1.0999             nan     0.1000   -0.0234
##    200        0.9947             nan     0.1000   -0.0285
##    220        0.8928             nan     0.1000   -0.0045
##    240        0.8237             nan     0.1000   -0.0141
##    260        0.7514             nan     0.1000   -0.0229
##    280        0.6977             nan     0.1000   -0.0150
##    300        0.6397             nan     0.1000   -0.0051
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.5243             nan     0.1000   10.3151
##      2       43.5037             nan     0.1000    8.0391
##      3       36.3146             nan     0.1000    6.2526
##      4       30.5839             nan     0.1000    5.7645
##      5       25.9360             nan     0.1000    3.9629
##      6       22.1256             nan     0.1000    3.4614
##      7       18.9333             nan     0.1000    2.8435
##      8       16.1183             nan     0.1000    2.3615
##      9       13.7670             nan     0.1000    2.0181
##     10       12.1843             nan     0.1000    1.6366
##     20        4.7071             nan     0.1000    0.2993
##     40        3.0248             nan     0.1000   -0.0216
##     60        2.5585             nan     0.1000   -0.0063
##     80        2.2927             nan     0.1000   -0.0548
##    100        2.0388             nan     0.1000   -0.0346
##    120        1.8565             nan     0.1000   -0.0123
##    140        1.7084             nan     0.1000   -0.0177
##    160        1.6002             nan     0.1000   -0.0267
##    180        1.4884             nan     0.1000   -0.0169
##    200        1.3894             nan     0.1000   -0.0094
##    220        1.3004             nan     0.1000   -0.0077
##    240        1.2206             nan     0.1000   -0.0155
##    260        1.1393             nan     0.1000   -0.0153
##    280        1.0749             nan     0.1000   -0.0144
##    300        1.0164             nan     0.1000   -0.0108
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.5310             nan     0.0100    0.7276
##      2       58.7274             nan     0.0100    0.7540
##      3       58.0097             nan     0.0100    0.7208
##      4       57.2996             nan     0.0100    0.6594
##      5       56.5967             nan     0.0100    0.6803
##      6       55.9249             nan     0.0100    0.6456
##      7       55.2071             nan     0.0100    0.6612
##      8       54.5822             nan     0.0100    0.6166
##      9       53.9591             nan     0.0100    0.6489
##     10       53.3008             nan     0.0100    0.6375
##     20       47.3029             nan     0.0100    0.5035
##     40       38.1055             nan     0.0100    0.3775
##     60       31.2738             nan     0.0100    0.2812
##     80       26.0247             nan     0.0100    0.2264
##    100       21.9756             nan     0.0100    0.1770
##    120       18.8576             nan     0.0100    0.0893
##    140       16.4067             nan     0.0100    0.0491
##    160       14.3861             nan     0.0100    0.0727
##    180       12.7174             nan     0.0100    0.0818
##    200       11.3596             nan     0.0100    0.0522
##    220       10.2164             nan     0.0100    0.0333
##    240        9.2647             nan     0.0100    0.0190
##    260        8.4918             nan     0.0100    0.0308
##    280        7.8139             nan     0.0100    0.0234
##    300        7.2264             nan     0.0100    0.0205
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.4608             nan     0.0100    0.7181
##      2       58.6655             nan     0.0100    0.7044
##      3       57.9136             nan     0.0100    0.7424
##      4       57.2325             nan     0.0100    0.7134
##      5       56.5630             nan     0.0100    0.7258
##      6       55.8832             nan     0.0100    0.6459
##      7       55.2360             nan     0.0100    0.6986
##      8       54.6031             nan     0.0100    0.6654
##      9       53.9255             nan     0.0100    0.6405
##     10       53.2681             nan     0.0100    0.6429
##     20       47.4623             nan     0.0100    0.5270
##     40       38.4321             nan     0.0100    0.3985
##     60       31.5295             nan     0.0100    0.3064
##     80       26.2660             nan     0.0100    0.2518
##    100       22.1788             nan     0.0100    0.1801
##    120       18.9704             nan     0.0100    0.0960
##    140       16.4751             nan     0.0100    0.0947
##    160       14.4683             nan     0.0100    0.0766
##    180       12.7725             nan     0.0100    0.0624
##    200       11.4226             nan     0.0100    0.0514
##    220       10.3344             nan     0.0100    0.0391
##    240        9.3668             nan     0.0100    0.0276
##    260        8.5621             nan     0.0100    0.0299
##    280        7.8826             nan     0.0100    0.0246
##    300        7.2998             nan     0.0100    0.0163
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.4931             nan     0.0100    0.7427
##      2       58.7447             nan     0.0100    0.7792
##      3       58.0285             nan     0.0100    0.7712
##      4       57.3424             nan     0.0100    0.7535
##      5       56.6934             nan     0.0100    0.6807
##      6       56.0248             nan     0.0100    0.6733
##      7       55.3520             nan     0.0100    0.6468
##      8       54.6651             nan     0.0100    0.6637
##      9       54.0678             nan     0.0100    0.6154
##     10       53.5092             nan     0.0100    0.6202
##     20       47.6536             nan     0.0100    0.4539
##     40       38.5259             nan     0.0100    0.3714
##     60       31.5008             nan     0.0100    0.2844
##     80       26.2746             nan     0.0100    0.2227
##    100       22.0520             nan     0.0100    0.1755
##    120       18.9481             nan     0.0100    0.1335
##    140       16.5071             nan     0.0100    0.1044
##    160       14.4742             nan     0.0100    0.0697
##    180       12.8642             nan     0.0100    0.0716
##    200       11.5470             nan     0.0100    0.0503
##    220       10.4247             nan     0.0100    0.0510
##    240        9.5027             nan     0.0100    0.0409
##    260        8.7361             nan     0.0100    0.0266
##    280        8.0682             nan     0.0100    0.0226
##    300        7.4985             nan     0.0100    0.0252
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.2562             nan     0.0100    0.9495
##      2       58.2776             nan     0.0100    0.8652
##      3       57.2564             nan     0.0100    0.9598
##      4       56.2957             nan     0.0100    0.9446
##      5       55.3454             nan     0.0100    0.8770
##      6       54.4817             nan     0.0100    0.9847
##      7       53.5707             nan     0.0100    0.8120
##      8       52.7068             nan     0.0100    0.9104
##      9       51.8652             nan     0.0100    0.8198
##     10       50.9812             nan     0.0100    0.8332
##     20       43.3049             nan     0.0100    0.6795
##     40       31.8561             nan     0.0100    0.4527
##     60       23.8294             nan     0.0100    0.2563
##     80       18.3028             nan     0.0100    0.1789
##    100       14.3420             nan     0.0100    0.1493
##    120       11.5584             nan     0.0100    0.1105
##    140        9.3850             nan     0.0100    0.0914
##    160        7.8056             nan     0.0100    0.0626
##    180        6.6584             nan     0.0100    0.0417
##    200        5.7627             nan     0.0100    0.0294
##    220        5.1003             nan     0.0100    0.0192
##    240        4.5476             nan     0.0100    0.0150
##    260        4.1614             nan     0.0100    0.0095
##    280        3.8294             nan     0.0100    0.0100
##    300        3.5996             nan     0.0100    0.0057
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.2168             nan     0.0100    0.8972
##      2       58.2118             nan     0.0100    1.0036
##      3       57.2523             nan     0.0100    0.9117
##      4       56.3160             nan     0.0100    1.0042
##      5       55.3696             nan     0.0100    0.8439
##      6       54.4433             nan     0.0100    0.7690
##      7       53.5090             nan     0.0100    0.8167
##      8       52.6270             nan     0.0100    0.7579
##      9       51.8101             nan     0.0100    0.8046
##     10       50.9744             nan     0.0100    0.8645
##     20       43.2930             nan     0.0100    0.7352
##     40       31.9709             nan     0.0100    0.4822
##     60       23.9414             nan     0.0100    0.3245
##     80       18.3799             nan     0.0100    0.2293
##    100       14.4788             nan     0.0100    0.1678
##    120       11.6612             nan     0.0100    0.0833
##    140        9.5193             nan     0.0100    0.0848
##    160        7.8943             nan     0.0100    0.0562
##    180        6.7135             nan     0.0100    0.0483
##    200        5.8479             nan     0.0100    0.0352
##    220        5.1854             nan     0.0100    0.0230
##    240        4.6985             nan     0.0100    0.0126
##    260        4.3008             nan     0.0100    0.0111
##    280        4.0089             nan     0.0100    0.0041
##    300        3.7798             nan     0.0100    0.0073
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.2672             nan     0.0100    1.0476
##      2       58.2366             nan     0.0100    1.0227
##      3       57.2547             nan     0.0100    0.8995
##      4       56.2928             nan     0.0100    0.8597
##      5       55.3744             nan     0.0100    0.8448
##      6       54.4875             nan     0.0100    0.8610
##      7       53.6227             nan     0.0100    0.8626
##      8       52.7994             nan     0.0100    0.8165
##      9       51.9697             nan     0.0100    0.8049
##     10       51.1370             nan     0.0100    0.7252
##     20       43.4795             nan     0.0100    0.7595
##     40       32.0588             nan     0.0100    0.4886
##     60       24.0832             nan     0.0100    0.3181
##     80       18.5443             nan     0.0100    0.1998
##    100       14.6046             nan     0.0100    0.1416
##    120       11.8787             nan     0.0100    0.0990
##    140        9.7991             nan     0.0100    0.0829
##    160        8.2642             nan     0.0100    0.0593
##    180        7.0519             nan     0.0100    0.0470
##    200        6.1789             nan     0.0100    0.0273
##    220        5.4887             nan     0.0100    0.0203
##    240        4.9829             nan     0.0100    0.0135
##    260        4.6013             nan     0.0100    0.0079
##    280        4.2946             nan     0.0100    0.0088
##    300        4.0803             nan     0.0100    0.0046
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.1660             nan     0.0100    1.0533
##      2       58.1259             nan     0.0100    1.1299
##      3       57.0723             nan     0.0100    1.1273
##      4       56.0621             nan     0.0100    0.8128
##      5       55.0832             nan     0.0100    0.8950
##      6       54.1275             nan     0.0100    0.9705
##      7       53.2139             nan     0.0100    0.8460
##      8       52.2474             nan     0.0100    0.8900
##      9       51.3724             nan     0.0100    0.9093
##     10       50.5048             nan     0.0100    0.9553
##     20       42.4873             nan     0.0100    0.7225
##     40       30.4558             nan     0.0100    0.4640
##     60       22.1976             nan     0.0100    0.3334
##     80       16.3987             nan     0.0100    0.2039
##    100       12.4337             nan     0.0100    0.1314
##    120        9.7016             nan     0.0100    0.1237
##    140        7.7292             nan     0.0100    0.0744
##    160        6.3353             nan     0.0100    0.0539
##    180        5.3284             nan     0.0100    0.0429
##    200        4.5739             nan     0.0100    0.0261
##    220        4.0087             nan     0.0100    0.0227
##    240        3.5926             nan     0.0100    0.0081
##    260        3.2951             nan     0.0100    0.0033
##    280        3.0562             nan     0.0100    0.0021
##    300        2.8713             nan     0.0100    0.0046
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.1759             nan     0.0100    1.0092
##      2       58.1542             nan     0.0100    1.0883
##      3       57.1099             nan     0.0100    1.0436
##      4       56.1356             nan     0.0100    0.9374
##      5       55.1770             nan     0.0100    0.8702
##      6       54.2092             nan     0.0100    1.0263
##      7       53.3252             nan     0.0100    0.9006
##      8       52.4076             nan     0.0100    0.9524
##      9       51.5204             nan     0.0100    0.8619
##     10       50.6685             nan     0.0100    0.8119
##     20       42.6965             nan     0.0100    0.7701
##     40       30.5920             nan     0.0100    0.5110
##     60       22.3897             nan     0.0100    0.3454
##     80       16.7214             nan     0.0100    0.2645
##    100       12.6832             nan     0.0100    0.1565
##    120        9.8446             nan     0.0100    0.1136
##    140        7.8585             nan     0.0100    0.0836
##    160        6.4842             nan     0.0100    0.0524
##    180        5.4501             nan     0.0100    0.0426
##    200        4.7094             nan     0.0100    0.0240
##    220        4.1711             nan     0.0100    0.0131
##    240        3.7629             nan     0.0100    0.0041
##    260        3.4784             nan     0.0100    0.0064
##    280        3.2599             nan     0.0100    0.0016
##    300        3.0861             nan     0.0100   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.1830             nan     0.0100    0.9324
##      2       58.1254             nan     0.0100    0.9812
##      3       57.1255             nan     0.0100    0.9642
##      4       56.1621             nan     0.0100    0.9591
##      5       55.2049             nan     0.0100    0.8737
##      6       54.2480             nan     0.0100    0.9627
##      7       53.3274             nan     0.0100    0.8895
##      8       52.4552             nan     0.0100    0.8572
##      9       51.5569             nan     0.0100    0.8378
##     10       50.6552             nan     0.0100    0.8811
##     20       42.6543             nan     0.0100    0.7007
##     40       30.7191             nan     0.0100    0.4778
##     60       22.5604             nan     0.0100    0.3193
##     80       16.8728             nan     0.0100    0.2143
##    100       12.9632             nan     0.0100    0.1605
##    120       10.1685             nan     0.0100    0.1139
##    140        8.2640             nan     0.0100    0.0662
##    160        6.9085             nan     0.0100    0.0561
##    180        5.8667             nan     0.0100    0.0390
##    200        5.1421             nan     0.0100    0.0260
##    220        4.6250             nan     0.0100    0.0159
##    240        4.2565             nan     0.0100    0.0059
##    260        3.9693             nan     0.0100    0.0150
##    280        3.7530             nan     0.0100    0.0036
##    300        3.5810             nan     0.0100    0.0023
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.2172             nan     0.0500    3.5345
##      2       53.0580             nan     0.0500    3.3131
##      3       50.0289             nan     0.0500    3.1580
##      4       47.3257             nan     0.0500    2.4752
##      5       44.7458             nan     0.0500    2.2671
##      6       42.1244             nan     0.0500    2.3969
##      7       39.9029             nan     0.0500    2.2012
##      8       38.0133             nan     0.0500    1.8091
##      9       36.1339             nan     0.0500    1.7790
##     10       34.3214             nan     0.0500    1.8085
##     20       21.9802             nan     0.0500    0.7899
##     40       11.5176             nan     0.0500    0.2089
##     60        7.2705             nan     0.0500    0.0793
##     80        5.3203             nan     0.0500    0.0324
##    100        4.4005             nan     0.0500    0.0020
##    120        3.8871             nan     0.0500   -0.0012
##    140        3.6344             nan     0.0500    0.0057
##    160        3.4908             nan     0.0500   -0.0004
##    180        3.3927             nan     0.0500   -0.0053
##    200        3.2918             nan     0.0500   -0.0019
##    220        3.2162             nan     0.0500   -0.0043
##    240        3.1590             nan     0.0500   -0.0041
##    260        3.0991             nan     0.0500   -0.0087
##    280        3.0532             nan     0.0500   -0.0022
##    300        3.0042             nan     0.0500   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.5490             nan     0.0500    3.4438
##      2       53.2862             nan     0.0500    3.4542
##      3       50.4501             nan     0.0500    2.4023
##      4       47.5761             nan     0.0500    2.8047
##      5       44.8229             nan     0.0500    2.5701
##      6       42.2079             nan     0.0500    2.3402
##      7       39.6999             nan     0.0500    2.3014
##      8       37.4855             nan     0.0500    1.9654
##      9       35.5773             nan     0.0500    1.9692
##     10       33.7154             nan     0.0500    1.6105
##     20       21.7557             nan     0.0500    0.8792
##     40       11.3053             nan     0.0500    0.2809
##     60        7.4102             nan     0.0500    0.0932
##     80        5.5168             nan     0.0500    0.0281
##    100        4.6080             nan     0.0500   -0.0069
##    120        4.1156             nan     0.0500    0.0085
##    140        3.8375             nan     0.0500    0.0029
##    160        3.6796             nan     0.0500   -0.0032
##    180        3.5564             nan     0.0500   -0.0047
##    200        3.4547             nan     0.0500   -0.0022
##    220        3.3867             nan     0.0500   -0.0022
##    240        3.3141             nan     0.0500   -0.0023
##    260        3.2498             nan     0.0500   -0.0153
##    280        3.1983             nan     0.0500   -0.0067
##    300        3.1387             nan     0.0500   -0.0061
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.4512             nan     0.0500    3.8397
##      2       53.1691             nan     0.0500    3.3514
##      3       50.0897             nan     0.0500    3.0425
##      4       47.3378             nan     0.0500    2.4967
##      5       44.6938             nan     0.0500    2.4810
##      6       42.4108             nan     0.0500    2.4061
##      7       40.1914             nan     0.0500    1.8212
##      8       38.1419             nan     0.0500    1.9198
##      9       36.3059             nan     0.0500    1.8363
##     10       34.4816             nan     0.0500    1.4315
##     20       21.7611             nan     0.0500    0.9200
##     40       11.2906             nan     0.0500    0.2416
##     60        7.3961             nan     0.0500    0.1041
##     80        5.5350             nan     0.0500    0.0360
##    100        4.6021             nan     0.0500    0.0282
##    120        4.1641             nan     0.0500    0.0127
##    140        3.9733             nan     0.0500   -0.0040
##    160        3.8452             nan     0.0500    0.0015
##    180        3.7473             nan     0.0500    0.0054
##    200        3.6667             nan     0.0500   -0.0151
##    220        3.5911             nan     0.0500   -0.0088
##    240        3.5337             nan     0.0500   -0.0004
##    260        3.4694             nan     0.0500   -0.0093
##    280        3.4218             nan     0.0500   -0.0064
##    300        3.3818             nan     0.0500   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.3105             nan     0.0500    4.6992
##      2       50.8135             nan     0.0500    4.9273
##      3       46.7715             nan     0.0500    3.6046
##      4       43.2418             nan     0.0500    3.4671
##      5       39.8614             nan     0.0500    2.9666
##      6       36.7503             nan     0.0500    2.6636
##      7       34.0464             nan     0.0500    2.6040
##      8       31.5051             nan     0.0500    2.5223
##      9       29.1072             nan     0.0500    2.3144
##     10       26.9775             nan     0.0500    1.7371
##     20       13.9623             nan     0.0500    0.8471
##     40        5.7412             nan     0.0500    0.1827
##     60        3.6319             nan     0.0500    0.0399
##     80        2.9357             nan     0.0500    0.0072
##    100        2.5925             nan     0.0500   -0.0149
##    120        2.3684             nan     0.0500   -0.0146
##    140        2.2093             nan     0.0500   -0.0125
##    160        2.0621             nan     0.0500   -0.0024
##    180        1.9443             nan     0.0500   -0.0153
##    200        1.8580             nan     0.0500   -0.0146
##    220        1.7489             nan     0.0500   -0.0072
##    240        1.6616             nan     0.0500   -0.0111
##    260        1.6007             nan     0.0500   -0.0121
##    280        1.5365             nan     0.0500   -0.0064
##    300        1.4736             nan     0.0500   -0.0054
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.3460             nan     0.0500    4.8082
##      2       51.1024             nan     0.0500    4.1978
##      3       46.9398             nan     0.0500    3.9122
##      4       43.1605             nan     0.0500    3.4750
##      5       39.6809             nan     0.0500    3.5362
##      6       36.7364             nan     0.0500    2.6174
##      7       34.0597             nan     0.0500    2.5813
##      8       31.5075             nan     0.0500    2.3642
##      9       29.3071             nan     0.0500    2.1936
##     10       27.2265             nan     0.0500    1.8889
##     20       14.0143             nan     0.0500    0.8055
##     40        5.7156             nan     0.0500    0.1698
##     60        3.7682             nan     0.0500    0.0161
##     80        3.1659             nan     0.0500   -0.0168
##    100        2.8759             nan     0.0500    0.0044
##    120        2.6875             nan     0.0500   -0.0132
##    140        2.5153             nan     0.0500   -0.0075
##    160        2.3933             nan     0.0500   -0.0151
##    180        2.2462             nan     0.0500   -0.0121
##    200        2.1533             nan     0.0500   -0.0092
##    220        2.0818             nan     0.0500   -0.0031
##    240        1.9915             nan     0.0500   -0.0122
##    260        1.9341             nan     0.0500   -0.0136
##    280        1.8615             nan     0.0500   -0.0121
##    300        1.7806             nan     0.0500   -0.0056
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.4673             nan     0.0500    4.8546
##      2       51.0004             nan     0.0500    4.7062
##      3       46.8684             nan     0.0500    3.8162
##      4       43.1961             nan     0.0500    3.6055
##      5       39.7377             nan     0.0500    3.2091
##      6       36.9828             nan     0.0500    2.8802
##      7       34.2291             nan     0.0500    2.8777
##      8       31.6605             nan     0.0500    2.1394
##      9       29.4957             nan     0.0500    2.4222
##     10       27.3567             nan     0.0500    2.0692
##     20       14.2855             nan     0.0500    0.6694
##     40        6.1202             nan     0.0500    0.1544
##     60        4.1146             nan     0.0500    0.0206
##     80        3.4638             nan     0.0500   -0.0198
##    100        3.1951             nan     0.0500   -0.0115
##    120        3.0209             nan     0.0500   -0.0064
##    140        2.8661             nan     0.0500   -0.0085
##    160        2.7551             nan     0.0500   -0.0093
##    180        2.6418             nan     0.0500   -0.0082
##    200        2.5480             nan     0.0500   -0.0204
##    220        2.4556             nan     0.0500   -0.0033
##    240        2.3835             nan     0.0500   -0.0134
##    260        2.3073             nan     0.0500   -0.0202
##    280        2.2169             nan     0.0500   -0.0109
##    300        2.1496             nan     0.0500   -0.0030
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.1857             nan     0.0500    4.8837
##      2       50.5688             nan     0.0500    4.1611
##      3       46.3689             nan     0.0500    4.7456
##      4       42.4907             nan     0.0500    3.8934
##      5       39.0269             nan     0.0500    3.1725
##      6       35.8638             nan     0.0500    2.9448
##      7       32.9522             nan     0.0500    2.9695
##      8       30.4349             nan     0.0500    2.5099
##      9       28.2084             nan     0.0500    2.2963
##     10       26.1367             nan     0.0500    2.2792
##     20       12.3870             nan     0.0500    0.7144
##     40        4.5159             nan     0.0500    0.1651
##     60        2.8716             nan     0.0500    0.0297
##     80        2.3045             nan     0.0500   -0.0115
##    100        1.9737             nan     0.0500   -0.0124
##    120        1.7930             nan     0.0500   -0.0118
##    140        1.6307             nan     0.0500   -0.0118
##    160        1.4851             nan     0.0500   -0.0045
##    180        1.3631             nan     0.0500   -0.0117
##    200        1.2643             nan     0.0500   -0.0136
##    220        1.1881             nan     0.0500   -0.0091
##    240        1.0961             nan     0.0500   -0.0063
##    260        1.0162             nan     0.0500   -0.0065
##    280        0.9550             nan     0.0500   -0.0126
##    300        0.8910             nan     0.0500   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.9232             nan     0.0500    5.3887
##      2       50.0496             nan     0.0500    4.9480
##      3       45.7527             nan     0.0500    4.1426
##      4       41.8920             nan     0.0500    4.1011
##      5       38.1925             nan     0.0500    3.4646
##      6       35.0637             nan     0.0500    3.4100
##      7       32.3189             nan     0.0500    2.8284
##      8       29.6359             nan     0.0500    2.8051
##      9       27.2683             nan     0.0500    2.7251
##     10       25.3115             nan     0.0500    2.0270
##     20       12.1808             nan     0.0500    0.7856
##     40        4.5920             nan     0.0500    0.0962
##     60        3.0367             nan     0.0500    0.0119
##     80        2.5324             nan     0.0500   -0.0127
##    100        2.2956             nan     0.0500   -0.0173
##    120        2.1016             nan     0.0500   -0.0085
##    140        1.9468             nan     0.0500   -0.0129
##    160        1.8193             nan     0.0500   -0.0090
##    180        1.6931             nan     0.0500   -0.0049
##    200        1.5853             nan     0.0500   -0.0100
##    220        1.4886             nan     0.0500   -0.0144
##    240        1.4078             nan     0.0500   -0.0171
##    260        1.3378             nan     0.0500   -0.0051
##    280        1.2736             nan     0.0500   -0.0133
##    300        1.2098             nan     0.0500   -0.0065
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.3440             nan     0.0500    5.2930
##      2       50.6264             nan     0.0500    4.0565
##      3       46.1377             nan     0.0500    3.7722
##      4       42.3810             nan     0.0500    3.9496
##      5       38.8966             nan     0.0500    3.2894
##      6       35.7395             nan     0.0500    3.0532
##      7       32.7523             nan     0.0500    2.7270
##      8       30.1362             nan     0.0500    2.4489
##      9       27.8266             nan     0.0500    2.5671
##     10       25.8540             nan     0.0500    2.0192
##     20       12.7242             nan     0.0500    0.7560
##     40        4.9571             nan     0.0500    0.0860
##     60        3.5689             nan     0.0500    0.0199
##     80        3.0792             nan     0.0500   -0.0008
##    100        2.7888             nan     0.0500   -0.0108
##    120        2.5822             nan     0.0500   -0.0168
##    140        2.4062             nan     0.0500   -0.0144
##    160        2.2650             nan     0.0500   -0.0160
##    180        2.1387             nan     0.0500   -0.0104
##    200        2.0378             nan     0.0500   -0.0226
##    220        1.9611             nan     0.0500   -0.0134
##    240        1.8733             nan     0.0500   -0.0130
##    260        1.7797             nan     0.0500   -0.0064
##    280        1.7152             nan     0.0500   -0.0095
##    300        1.6319             nan     0.0500   -0.0218
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.4413             nan     0.1000    7.0482
##      2       46.9345             nan     0.1000    6.0775
##      3       42.1806             nan     0.1000    4.9367
##      4       37.7369             nan     0.1000    3.8293
##      5       34.1193             nan     0.1000    3.6720
##      6       30.9113             nan     0.1000    3.1331
##      7       28.2375             nan     0.1000    2.3688
##      8       25.6355             nan     0.1000    2.0058
##      9       23.4848             nan     0.1000    1.9998
##     10       21.5960             nan     0.1000    1.8265
##     20       11.2122             nan     0.1000    0.5703
##     40        5.5294             nan     0.1000    0.0751
##     60        4.0125             nan     0.1000    0.0272
##     80        3.5826             nan     0.1000   -0.0205
##    100        3.3283             nan     0.1000   -0.0180
##    120        3.1888             nan     0.1000   -0.0183
##    140        3.0648             nan     0.1000    0.0043
##    160        2.9676             nan     0.1000   -0.0046
##    180        2.8878             nan     0.1000   -0.0253
##    200        2.8500             nan     0.1000   -0.0109
##    220        2.7893             nan     0.1000   -0.0068
##    240        2.7399             nan     0.1000   -0.0125
##    260        2.6782             nan     0.1000   -0.0181
##    280        2.6396             nan     0.1000   -0.0284
##    300        2.5996             nan     0.1000   -0.0092
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.2113             nan     0.1000    7.2185
##      2       47.1566             nan     0.1000    5.7679
##      3       42.4754             nan     0.1000    4.8436
##      4       38.4897             nan     0.1000    3.7088
##      5       34.5489             nan     0.1000    4.0258
##      6       31.0548             nan     0.1000    3.4631
##      7       28.3184             nan     0.1000    2.6479
##      8       25.9242             nan     0.1000    2.3159
##      9       23.7075             nan     0.1000    2.3142
##     10       21.6945             nan     0.1000    1.4562
##     20       11.0545             nan     0.1000    0.4796
##     40        5.3072             nan     0.1000    0.0443
##     60        4.0285             nan     0.1000    0.0102
##     80        3.6023             nan     0.1000    0.0107
##    100        3.3816             nan     0.1000   -0.0131
##    120        3.2236             nan     0.1000   -0.0061
##    140        3.1159             nan     0.1000   -0.0056
##    160        3.0313             nan     0.1000   -0.0047
##    180        2.9501             nan     0.1000   -0.0023
##    200        2.9048             nan     0.1000   -0.0199
##    220        2.8509             nan     0.1000   -0.0022
##    240        2.7995             nan     0.1000    0.0019
##    260        2.7515             nan     0.1000   -0.0059
##    280        2.7220             nan     0.1000   -0.0070
##    300        2.6959             nan     0.1000   -0.0108
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.8100             nan     0.1000    7.3750
##      2       46.6933             nan     0.1000    5.6288
##      3       42.1308             nan     0.1000    4.7390
##      4       37.9086             nan     0.1000    4.4668
##      5       33.9932             nan     0.1000    3.5485
##      6       30.6513             nan     0.1000    2.8972
##      7       27.9182             nan     0.1000    2.4862
##      8       25.3093             nan     0.1000    2.3595
##      9       23.3493             nan     0.1000    1.8550
##     10       21.4531             nan     0.1000    2.0171
##     20       11.1004             nan     0.1000    0.5512
##     40        5.7002             nan     0.1000    0.0896
##     60        4.5496             nan     0.1000   -0.0396
##     80        4.1940             nan     0.1000   -0.0272
##    100        3.9786             nan     0.1000    0.0008
##    120        3.8166             nan     0.1000   -0.0010
##    140        3.6624             nan     0.1000   -0.0052
##    160        3.5650             nan     0.1000   -0.0129
##    180        3.4765             nan     0.1000   -0.0209
##    200        3.3868             nan     0.1000   -0.0115
##    220        3.3098             nan     0.1000   -0.0210
##    240        3.2425             nan     0.1000   -0.0056
##    260        3.1999             nan     0.1000   -0.0250
##    280        3.1466             nan     0.1000   -0.0046
##    300        3.0790             nan     0.1000   -0.0271
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.1775             nan     0.1000    9.4080
##      2       43.8478             nan     0.1000    7.6566
##      3       37.2354             nan     0.1000    6.7593
##      4       32.0868             nan     0.1000    5.3496
##      5       27.7231             nan     0.1000    3.8389
##      6       23.8354             nan     0.1000    3.4698
##      7       20.7364             nan     0.1000    2.8912
##      8       18.2526             nan     0.1000    2.3685
##      9       16.0634             nan     0.1000    1.8626
##     10       14.3530             nan     0.1000    1.8486
##     20        5.7293             nan     0.1000    0.3496
##     40        3.0684             nan     0.1000   -0.0380
##     60        2.5188             nan     0.1000   -0.0051
##     80        2.1796             nan     0.1000   -0.0119
##    100        1.9204             nan     0.1000   -0.0369
##    120        1.6951             nan     0.1000   -0.0180
##    140        1.5568             nan     0.1000   -0.0047
##    160        1.4297             nan     0.1000   -0.0074
##    180        1.3127             nan     0.1000   -0.0126
##    200        1.1961             nan     0.1000   -0.0097
##    220        1.1163             nan     0.1000   -0.0077
##    240        1.0486             nan     0.1000   -0.0216
##    260        0.9690             nan     0.1000   -0.0203
##    280        0.9110             nan     0.1000   -0.0192
##    300        0.8538             nan     0.1000   -0.0098
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.6595             nan     0.1000    9.9095
##      2       42.9095             nan     0.1000    7.9389
##      3       36.5242             nan     0.1000    6.2546
##      4       31.4446             nan     0.1000    4.7792
##      5       26.5409             nan     0.1000    4.5327
##      6       23.0049             nan     0.1000    3.4396
##      7       20.1019             nan     0.1000    2.6274
##      8       17.6506             nan     0.1000    2.2507
##      9       15.5127             nan     0.1000    1.6599
##     10       13.9107             nan     0.1000    1.5237
##     20        5.8154             nan     0.1000    0.3539
##     40        3.1334             nan     0.1000   -0.0066
##     60        2.6766             nan     0.1000   -0.0162
##     80        2.4024             nan     0.1000   -0.0175
##    100        2.2158             nan     0.1000   -0.0592
##    120        2.0456             nan     0.1000   -0.0226
##    140        1.9040             nan     0.1000   -0.0156
##    160        1.7839             nan     0.1000   -0.0246
##    180        1.6838             nan     0.1000   -0.0288
##    200        1.5742             nan     0.1000   -0.0162
##    220        1.4782             nan     0.1000   -0.0133
##    240        1.4086             nan     0.1000   -0.0134
##    260        1.3391             nan     0.1000   -0.0210
##    280        1.2640             nan     0.1000   -0.0106
##    300        1.1993             nan     0.1000   -0.0079
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.4600             nan     0.1000    9.2256
##      2       42.8153             nan     0.1000    7.4669
##      3       36.6060             nan     0.1000    6.2964
##      4       31.3247             nan     0.1000    5.5919
##      5       27.0207             nan     0.1000    4.1177
##      6       23.5415             nan     0.1000    3.0755
##      7       20.6148             nan     0.1000    2.8223
##      8       18.1067             nan     0.1000    1.9492
##      9       15.8204             nan     0.1000    2.0795
##     10       14.2154             nan     0.1000    1.6097
##     20        6.1504             nan     0.1000    0.3035
##     40        3.6257             nan     0.1000    0.0059
##     60        3.1902             nan     0.1000   -0.0181
##     80        2.8918             nan     0.1000   -0.0603
##    100        2.6322             nan     0.1000   -0.0145
##    120        2.4311             nan     0.1000   -0.0105
##    140        2.2845             nan     0.1000   -0.0244
##    160        2.1437             nan     0.1000   -0.0340
##    180        2.0417             nan     0.1000   -0.0266
##    200        1.9398             nan     0.1000   -0.0200
##    220        1.8479             nan     0.1000   -0.0156
##    240        1.7231             nan     0.1000   -0.0198
##    260        1.6472             nan     0.1000   -0.0202
##    280        1.5822             nan     0.1000   -0.0149
##    300        1.5092             nan     0.1000   -0.0172
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.1278             nan     0.1000   10.7639
##      2       41.9169             nan     0.1000    7.7750
##      3       34.8019             nan     0.1000    6.7118
##      4       29.0119             nan     0.1000    5.8775
##      5       24.5851             nan     0.1000    3.9730
##      6       21.0382             nan     0.1000    3.7615
##      7       18.2798             nan     0.1000    2.5555
##      8       15.6987             nan     0.1000    2.2117
##      9       13.7514             nan     0.1000    1.8226
##     10       11.9778             nan     0.1000    1.7120
##     20        4.3139             nan     0.1000    0.2336
##     40        2.4684             nan     0.1000   -0.0115
##     60        1.9714             nan     0.1000   -0.0249
##     80        1.6010             nan     0.1000   -0.0175
##    100        1.3475             nan     0.1000   -0.0084
##    120        1.1572             nan     0.1000   -0.0148
##    140        0.9967             nan     0.1000   -0.0252
##    160        0.8725             nan     0.1000   -0.0107
##    180        0.7739             nan     0.1000   -0.0212
##    200        0.6847             nan     0.1000   -0.0145
##    220        0.6143             nan     0.1000   -0.0090
##    240        0.5348             nan     0.1000   -0.0105
##    260        0.4857             nan     0.1000   -0.0141
##    280        0.4352             nan     0.1000   -0.0110
##    300        0.3886             nan     0.1000   -0.0026
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.6709             nan     0.1000    9.8987
##      2       42.1694             nan     0.1000    7.3982
##      3       35.4002             nan     0.1000    6.2556
##      4       29.6558             nan     0.1000    5.4455
##      5       25.0474             nan     0.1000    4.9400
##      6       21.3173             nan     0.1000    3.3580
##      7       18.3288             nan     0.1000    2.7970
##      8       15.6790             nan     0.1000    2.3312
##      9       13.6829             nan     0.1000    2.0718
##     10       11.9804             nan     0.1000    1.7679
##     20        4.5127             nan     0.1000    0.3171
##     40        2.7647             nan     0.1000   -0.0138
##     60        2.2612             nan     0.1000   -0.0361
##     80        1.9090             nan     0.1000   -0.0370
##    100        1.6473             nan     0.1000   -0.0123
##    120        1.4679             nan     0.1000   -0.0437
##    140        1.3311             nan     0.1000   -0.0131
##    160        1.2084             nan     0.1000   -0.0261
##    180        1.1072             nan     0.1000   -0.0161
##    200        0.9940             nan     0.1000   -0.0125
##    220        0.9182             nan     0.1000   -0.0248
##    240        0.8526             nan     0.1000   -0.0100
##    260        0.7790             nan     0.1000   -0.0075
##    280        0.7199             nan     0.1000   -0.0123
##    300        0.6626             nan     0.1000   -0.0111
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.4166             nan     0.1000    9.7871
##      2       42.3009             nan     0.1000    7.8237
##      3       35.7272             nan     0.1000    7.6116
##      4       30.3339             nan     0.1000    5.4545
##      5       25.7600             nan     0.1000    4.6163
##      6       21.9639             nan     0.1000    3.6043
##      7       18.8351             nan     0.1000    2.8494
##      8       16.3420             nan     0.1000    2.4566
##      9       14.3548             nan     0.1000    1.8962
##     10       12.6935             nan     0.1000    1.5300
##     20        5.0710             nan     0.1000    0.2029
##     40        3.1581             nan     0.1000   -0.0205
##     60        2.6147             nan     0.1000   -0.0018
##     80        2.3475             nan     0.1000   -0.0460
##    100        2.1062             nan     0.1000   -0.0165
##    120        1.8971             nan     0.1000   -0.0157
##    140        1.7357             nan     0.1000   -0.0366
##    160        1.6097             nan     0.1000   -0.0175
##    180        1.4851             nan     0.1000   -0.0193
##    200        1.3963             nan     0.1000   -0.0271
##    220        1.3023             nan     0.1000   -0.0240
##    240        1.2026             nan     0.1000   -0.0187
##    260        1.1243             nan     0.1000   -0.0156
##    280        1.0559             nan     0.1000   -0.0148
##    300        0.9832             nan     0.1000   -0.0083
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.2250             nan     0.0100    0.8080
##      2       61.3759             nan     0.0100    0.7540
##      3       60.5950             nan     0.0100    0.7500
##      4       59.8375             nan     0.0100    0.7442
##      5       59.1443             nan     0.0100    0.7453
##      6       58.4077             nan     0.0100    0.7405
##      7       57.6459             nan     0.0100    0.7713
##      8       56.9483             nan     0.0100    0.6874
##      9       56.2531             nan     0.0100    0.7130
##     10       55.5600             nan     0.0100    0.6723
##     20       49.1877             nan     0.0100    0.5552
##     40       39.4816             nan     0.0100    0.3879
##     60       32.2214             nan     0.0100    0.3467
##     80       26.6893             nan     0.0100    0.2106
##    100       22.5130             nan     0.0100    0.1659
##    120       19.3795             nan     0.0100    0.1082
##    140       16.7304             nan     0.0100    0.0808
##    160       14.6506             nan     0.0100    0.0708
##    180       12.8703             nan     0.0100    0.0727
##    200       11.4987             nan     0.0100    0.0539
##    220       10.3751             nan     0.0100    0.0643
##    240        9.4425             nan     0.0100    0.0187
##    260        8.6003             nan     0.0100    0.0313
##    280        7.9025             nan     0.0100    0.0245
##    300        7.3315             nan     0.0100    0.0221
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.1775             nan     0.0100    0.7423
##      2       61.3921             nan     0.0100    0.7972
##      3       60.5523             nan     0.0100    0.7621
##      4       59.7922             nan     0.0100    0.7217
##      5       59.0384             nan     0.0100    0.7266
##      6       58.2876             nan     0.0100    0.6224
##      7       57.5883             nan     0.0100    0.7396
##      8       56.8261             nan     0.0100    0.6935
##      9       56.1379             nan     0.0100    0.7154
##     10       55.3895             nan     0.0100    0.6789
##     20       49.0788             nan     0.0100    0.5400
##     40       39.3837             nan     0.0100    0.3810
##     60       32.0102             nan     0.0100    0.2772
##     80       26.4681             nan     0.0100    0.1781
##    100       22.1948             nan     0.0100    0.1660
##    120       18.9428             nan     0.0100    0.1269
##    140       16.3706             nan     0.0100    0.1015
##    160       14.3054             nan     0.0100    0.0745
##    180       12.6997             nan     0.0100    0.0508
##    200       11.3268             nan     0.0100    0.0539
##    220       10.2240             nan     0.0100    0.0358
##    240        9.3001             nan     0.0100    0.0398
##    260        8.5537             nan     0.0100    0.0327
##    280        7.8663             nan     0.0100    0.0213
##    300        7.2647             nan     0.0100    0.0184
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.1290             nan     0.0100    0.8067
##      2       61.3155             nan     0.0100    0.7800
##      3       60.5204             nan     0.0100    0.7802
##      4       59.7427             nan     0.0100    0.7059
##      5       58.9736             nan     0.0100    0.7207
##      6       58.2447             nan     0.0100    0.7615
##      7       57.5576             nan     0.0100    0.7383
##      8       56.8487             nan     0.0100    0.7156
##      9       56.1289             nan     0.0100    0.6327
##     10       55.4179             nan     0.0100    0.6504
##     20       48.9773             nan     0.0100    0.5425
##     40       39.3361             nan     0.0100    0.4192
##     60       32.2009             nan     0.0100    0.2949
##     80       26.6833             nan     0.0100    0.2309
##    100       22.4815             nan     0.0100    0.1388
##    120       19.2056             nan     0.0100    0.1191
##    140       16.6507             nan     0.0100    0.0993
##    160       14.5210             nan     0.0100    0.0797
##    180       12.8835             nan     0.0100    0.0626
##    200       11.4841             nan     0.0100    0.0604
##    220       10.3122             nan     0.0100    0.0395
##    240        9.3963             nan     0.0100    0.0345
##    260        8.5962             nan     0.0100    0.0276
##    280        7.9332             nan     0.0100    0.0344
##    300        7.3494             nan     0.0100    0.0164
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.9338             nan     0.0100    1.0558
##      2       60.9236             nan     0.0100    0.9808
##      3       59.9626             nan     0.0100    0.9892
##      4       58.9619             nan     0.0100    1.0130
##      5       58.0657             nan     0.0100    0.8618
##      6       57.0910             nan     0.0100    0.8310
##      7       56.1803             nan     0.0100    0.9299
##      8       55.2932             nan     0.0100    0.8940
##      9       54.3656             nan     0.0100    0.8518
##     10       53.4433             nan     0.0100    0.8041
##     20       45.3767             nan     0.0100    0.6943
##     40       32.9992             nan     0.0100    0.4429
##     60       24.6285             nan     0.0100    0.3374
##     80       18.7587             nan     0.0100    0.2427
##    100       14.6550             nan     0.0100    0.1579
##    120       11.6727             nan     0.0100    0.1054
##    140        9.5370             nan     0.0100    0.0898
##    160        7.9515             nan     0.0100    0.0635
##    180        6.7901             nan     0.0100    0.0389
##    200        5.8707             nan     0.0100    0.0169
##    220        5.2179             nan     0.0100    0.0228
##    240        4.6995             nan     0.0100    0.0198
##    260        4.3069             nan     0.0100    0.0119
##    280        3.9777             nan     0.0100    0.0129
##    300        3.7251             nan     0.0100    0.0042
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.9252             nan     0.0100    1.0553
##      2       60.9329             nan     0.0100    1.0044
##      3       59.8752             nan     0.0100    0.8082
##      4       58.8918             nan     0.0100    1.0154
##      5       57.9231             nan     0.0100    0.9801
##      6       56.9689             nan     0.0100    0.9592
##      7       56.0481             nan     0.0100    0.9219
##      8       55.1753             nan     0.0100    0.9613
##      9       54.3024             nan     0.0100    0.8195
##     10       53.4295             nan     0.0100    0.9299
##     20       45.4755             nan     0.0100    0.6437
##     40       33.1642             nan     0.0100    0.4892
##     60       24.6195             nan     0.0100    0.3006
##     80       18.7592             nan     0.0100    0.2456
##    100       14.6244             nan     0.0100    0.1633
##    120       11.7325             nan     0.0100    0.1171
##    140        9.6111             nan     0.0100    0.0894
##    160        8.0001             nan     0.0100    0.0465
##    180        6.8428             nan     0.0100    0.0429
##    200        5.9700             nan     0.0100    0.0296
##    220        5.2908             nan     0.0100    0.0274
##    240        4.7608             nan     0.0100    0.0190
##    260        4.3717             nan     0.0100    0.0144
##    280        4.0627             nan     0.0100    0.0026
##    300        3.8414             nan     0.0100    0.0017
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.9034             nan     0.0100    1.0957
##      2       60.8535             nan     0.0100    1.1387
##      3       59.8447             nan     0.0100    1.0560
##      4       58.8592             nan     0.0100    1.0239
##      5       57.8631             nan     0.0100    0.8508
##      6       56.8872             nan     0.0100    0.8977
##      7       55.9178             nan     0.0100    0.9576
##      8       55.0172             nan     0.0100    0.8836
##      9       54.1250             nan     0.0100    0.9736
##     10       53.2791             nan     0.0100    0.8746
##     20       45.2750             nan     0.0100    0.6954
##     40       33.1562             nan     0.0100    0.4326
##     60       24.7718             nan     0.0100    0.3034
##     80       18.8867             nan     0.0100    0.2200
##    100       14.7351             nan     0.0100    0.1555
##    120       11.8158             nan     0.0100    0.1176
##    140        9.6989             nan     0.0100    0.0874
##    160        8.1425             nan     0.0100    0.0566
##    180        6.9508             nan     0.0100    0.0434
##    200        6.1180             nan     0.0100    0.0382
##    220        5.4361             nan     0.0100    0.0208
##    240        4.9255             nan     0.0100    0.0163
##    260        4.5287             nan     0.0100    0.0129
##    280        4.2315             nan     0.0100    0.0074
##    300        4.0083             nan     0.0100    0.0056
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.8840             nan     0.0100    1.1042
##      2       60.7707             nan     0.0100    1.0476
##      3       59.7050             nan     0.0100    0.9901
##      4       58.6289             nan     0.0100    0.9761
##      5       57.5941             nan     0.0100    1.0617
##      6       56.6050             nan     0.0100    0.8487
##      7       55.5825             nan     0.0100    0.7780
##      8       54.6082             nan     0.0100    0.9778
##      9       53.6413             nan     0.0100    0.8950
##     10       52.7058             nan     0.0100    0.8485
##     20       44.3335             nan     0.0100    0.7864
##     40       31.7128             nan     0.0100    0.5009
##     60       23.0027             nan     0.0100    0.3533
##     80       17.1075             nan     0.0100    0.2456
##    100       12.9496             nan     0.0100    0.1677
##    120       10.0180             nan     0.0100    0.1198
##    140        7.9927             nan     0.0100    0.0788
##    160        6.5216             nan     0.0100    0.0489
##    180        5.4869             nan     0.0100    0.0387
##    200        4.7378             nan     0.0100    0.0282
##    220        4.1741             nan     0.0100    0.0174
##    240        3.7455             nan     0.0100    0.0069
##    260        3.4255             nan     0.0100    0.0118
##    280        3.1851             nan     0.0100    0.0039
##    300        2.9918             nan     0.0100   -0.0033
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.9029             nan     0.0100    1.0838
##      2       60.8524             nan     0.0100    1.0358
##      3       59.8081             nan     0.0100    1.0544
##      4       58.7475             nan     0.0100    0.9792
##      5       57.6785             nan     0.0100    1.1190
##      6       56.6316             nan     0.0100    0.9687
##      7       55.6589             nan     0.0100    0.9899
##      8       54.7260             nan     0.0100    0.9178
##      9       53.7906             nan     0.0100    0.9544
##     10       52.8549             nan     0.0100    0.9613
##     20       44.4998             nan     0.0100    0.7896
##     40       31.8305             nan     0.0100    0.5433
##     60       23.1911             nan     0.0100    0.3793
##     80       17.1612             nan     0.0100    0.2516
##    100       13.0597             nan     0.0100    0.1660
##    120       10.1861             nan     0.0100    0.1142
##    140        8.0607             nan     0.0100    0.0813
##    160        6.6720             nan     0.0100    0.0446
##    180        5.6181             nan     0.0100    0.0284
##    200        4.8530             nan     0.0100    0.0289
##    220        4.3298             nan     0.0100    0.0119
##    240        3.9379             nan     0.0100    0.0124
##    260        3.6107             nan     0.0100    0.0093
##    280        3.3905             nan     0.0100    0.0069
##    300        3.2099             nan     0.0100    0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.8262             nan     0.0100    1.0282
##      2       60.7414             nan     0.0100    0.9300
##      3       59.7287             nan     0.0100    1.0469
##      4       58.7038             nan     0.0100    0.9661
##      5       57.6772             nan     0.0100    1.0138
##      6       56.7184             nan     0.0100    1.0387
##      7       55.7318             nan     0.0100    0.9497
##      8       54.7784             nan     0.0100    0.8475
##      9       53.8206             nan     0.0100    0.9277
##     10       52.8755             nan     0.0100    0.9244
##     20       44.5045             nan     0.0100    0.7444
##     40       31.9332             nan     0.0100    0.4698
##     60       23.2921             nan     0.0100    0.3239
##     80       17.3321             nan     0.0100    0.2311
##    100       13.2045             nan     0.0100    0.1453
##    120       10.3639             nan     0.0100    0.1160
##    140        8.3058             nan     0.0100    0.0827
##    160        6.8461             nan     0.0100    0.0571
##    180        5.8602             nan     0.0100    0.0313
##    200        5.0924             nan     0.0100    0.0289
##    220        4.5788             nan     0.0100    0.0199
##    240        4.1879             nan     0.0100    0.0120
##    260        3.9032             nan     0.0100    0.0097
##    280        3.6794             nan     0.0100    0.0018
##    300        3.5187             nan     0.0100    0.0035
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.1992             nan     0.0500    3.7633
##      2       55.8039             nan     0.0500    3.8103
##      3       52.4253             nan     0.0500    3.2032
##      4       49.5985             nan     0.0500    2.9521
##      5       46.9727             nan     0.0500    2.7187
##      6       44.4842             nan     0.0500    2.6494
##      7       42.5803             nan     0.0500    1.9475
##      8       40.0947             nan     0.0500    1.8446
##      9       38.0131             nan     0.0500    1.7675
##     10       36.3670             nan     0.0500    1.8697
##     20       22.7345             nan     0.0500    0.9666
##     40       11.5011             nan     0.0500    0.2560
##     60        7.3001             nan     0.0500    0.1227
##     80        5.4083             nan     0.0500    0.0197
##    100        4.4284             nan     0.0500    0.0123
##    120        3.9571             nan     0.0500    0.0030
##    140        3.7201             nan     0.0500    0.0027
##    160        3.5906             nan     0.0500   -0.0192
##    180        3.4824             nan     0.0500   -0.0068
##    200        3.4019             nan     0.0500   -0.0050
##    220        3.3336             nan     0.0500   -0.0081
##    240        3.2663             nan     0.0500   -0.0091
##    260        3.2168             nan     0.0500   -0.0033
##    280        3.1686             nan     0.0500   -0.0040
##    300        3.1305             nan     0.0500   -0.0039
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.8162             nan     0.0500    4.2199
##      2       54.8643             nan     0.0500    3.4326
##      3       51.5848             nan     0.0500    3.5052
##      4       48.7158             nan     0.0500    2.6763
##      5       46.0633             nan     0.0500    2.8754
##      6       43.5795             nan     0.0500    2.2300
##      7       41.1610             nan     0.0500    2.1220
##      8       38.8422             nan     0.0500    2.0800
##      9       36.7573             nan     0.0500    1.8519
##     10       34.9317             nan     0.0500    1.8425
##     20       21.6887             nan     0.0500    0.7019
##     40       11.0907             nan     0.0500    0.2682
##     60        7.1343             nan     0.0500    0.0684
##     80        5.3020             nan     0.0500    0.0193
##    100        4.4152             nan     0.0500    0.0124
##    120        4.0134             nan     0.0500    0.0157
##    140        3.7967             nan     0.0500    0.0009
##    160        3.6704             nan     0.0500    0.0033
##    180        3.5799             nan     0.0500    0.0016
##    200        3.5053             nan     0.0500   -0.0118
##    220        3.4409             nan     0.0500   -0.0051
##    240        3.3934             nan     0.0500   -0.0114
##    260        3.3438             nan     0.0500   -0.0073
##    280        3.2999             nan     0.0500   -0.0018
##    300        3.2459             nan     0.0500   -0.0071
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.4357             nan     0.0500    3.5867
##      2       55.5791             nan     0.0500    3.6253
##      3       52.4047             nan     0.0500    3.2569
##      4       49.1121             nan     0.0500    2.8698
##      5       46.2663             nan     0.0500    2.7743
##      6       43.5191             nan     0.0500    2.8597
##      7       41.1253             nan     0.0500    1.9907
##      8       38.7613             nan     0.0500    2.2448
##      9       36.6487             nan     0.0500    1.7755
##     10       34.5699             nan     0.0500    1.7496
##     20       22.0686             nan     0.0500    0.8324
##     40       11.3078             nan     0.0500    0.2082
##     60        7.2191             nan     0.0500    0.0738
##     80        5.3958             nan     0.0500    0.0586
##    100        4.5693             nan     0.0500    0.0168
##    120        4.1460             nan     0.0500    0.0076
##    140        3.9373             nan     0.0500    0.0060
##    160        3.8132             nan     0.0500   -0.0176
##    180        3.7193             nan     0.0500    0.0002
##    200        3.6579             nan     0.0500   -0.0018
##    220        3.5893             nan     0.0500   -0.0002
##    240        3.5393             nan     0.0500   -0.0145
##    260        3.4872             nan     0.0500   -0.0031
##    280        3.4536             nan     0.0500   -0.0060
##    300        3.4053             nan     0.0500   -0.0050
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.7751             nan     0.0500    5.4923
##      2       52.9072             nan     0.0500    4.4656
##      3       48.8432             nan     0.0500    4.1989
##      4       44.9278             nan     0.0500    4.0555
##      5       41.5002             nan     0.0500    3.5311
##      6       38.2291             nan     0.0500    3.2110
##      7       35.3868             nan     0.0500    2.9413
##      8       32.6454             nan     0.0500    2.7635
##      9       30.3262             nan     0.0500    2.3333
##     10       28.0794             nan     0.0500    2.0681
##     20       14.2866             nan     0.0500    0.7889
##     40        5.7829             nan     0.0500    0.1991
##     60        3.6033             nan     0.0500    0.0332
##     80        2.9941             nan     0.0500    0.0098
##    100        2.6870             nan     0.0500   -0.0096
##    120        2.5260             nan     0.0500   -0.0056
##    140        2.3545             nan     0.0500    0.0009
##    160        2.2110             nan     0.0500   -0.0150
##    180        2.0876             nan     0.0500   -0.0171
##    200        1.9880             nan     0.0500   -0.0159
##    220        1.8922             nan     0.0500   -0.0096
##    240        1.8132             nan     0.0500   -0.0145
##    260        1.7284             nan     0.0500   -0.0101
##    280        1.6496             nan     0.0500   -0.0081
##    300        1.5754             nan     0.0500   -0.0166
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.7875             nan     0.0500    5.2558
##      2       53.1611             nan     0.0500    4.5811
##      3       48.8136             nan     0.0500    4.1785
##      4       44.9329             nan     0.0500    3.3738
##      5       41.6542             nan     0.0500    3.0806
##      6       38.4826             nan     0.0500    2.9178
##      7       35.4393             nan     0.0500    3.0682
##      8       32.7793             nan     0.0500    2.6241
##      9       30.3208             nan     0.0500    2.4860
##     10       28.1855             nan     0.0500    1.9496
##     20       14.4514             nan     0.0500    0.8085
##     40        5.9206             nan     0.0500    0.1137
##     60        3.9843             nan     0.0500    0.0292
##     80        3.3317             nan     0.0500   -0.0128
##    100        3.0584             nan     0.0500   -0.0193
##    120        2.8309             nan     0.0500   -0.0124
##    140        2.6867             nan     0.0500   -0.0184
##    160        2.5395             nan     0.0500   -0.0179
##    180        2.4297             nan     0.0500   -0.0101
##    200        2.3229             nan     0.0500   -0.0101
##    220        2.2192             nan     0.0500   -0.0077
##    240        2.1290             nan     0.0500   -0.0175
##    260        2.0491             nan     0.0500   -0.0108
##    280        1.9831             nan     0.0500   -0.0050
##    300        1.9239             nan     0.0500   -0.0077
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.8154             nan     0.0500    4.7682
##      2       53.2368             nan     0.0500    4.7700
##      3       49.1382             nan     0.0500    4.2446
##      4       45.3072             nan     0.0500    3.7644
##      5       41.8973             nan     0.0500    3.4410
##      6       38.6171             nan     0.0500    2.9502
##      7       35.6895             nan     0.0500    2.8480
##      8       33.1026             nan     0.0500    2.4932
##      9       30.5648             nan     0.0500    2.1850
##     10       28.5676             nan     0.0500    2.1028
##     20       14.6785             nan     0.0500    0.8945
##     40        5.9461             nan     0.0500    0.1669
##     60        4.0232             nan     0.0500    0.0260
##     80        3.4272             nan     0.0500   -0.0083
##    100        3.1486             nan     0.0500   -0.0021
##    120        2.9701             nan     0.0500   -0.0131
##    140        2.8023             nan     0.0500   -0.0153
##    160        2.6882             nan     0.0500   -0.0219
##    180        2.5674             nan     0.0500   -0.0103
##    200        2.4610             nan     0.0500   -0.0146
##    220        2.3808             nan     0.0500   -0.0063
##    240        2.3006             nan     0.0500   -0.0073
##    260        2.2280             nan     0.0500   -0.0071
##    280        2.1721             nan     0.0500   -0.0162
##    300        2.1116             nan     0.0500   -0.0159
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.5632             nan     0.0500    5.6227
##      2       52.3703             nan     0.0500    5.1648
##      3       47.7481             nan     0.0500    4.3406
##      4       43.6565             nan     0.0500    4.5368
##      5       40.0569             nan     0.0500    2.9786
##      6       36.6729             nan     0.0500    3.5080
##      7       33.5570             nan     0.0500    2.9001
##      8       30.7604             nan     0.0500    2.6136
##      9       28.1671             nan     0.0500    2.3857
##     10       26.0534             nan     0.0500    1.7687
##     20       12.2958             nan     0.0500    0.7652
##     40        4.5136             nan     0.0500    0.1056
##     60        2.9138             nan     0.0500    0.0070
##     80        2.3907             nan     0.0500   -0.0314
##    100        2.0729             nan     0.0500   -0.0151
##    120        1.8810             nan     0.0500   -0.0203
##    140        1.7020             nan     0.0500   -0.0083
##    160        1.5390             nan     0.0500   -0.0087
##    180        1.4004             nan     0.0500   -0.0052
##    200        1.2840             nan     0.0500   -0.0032
##    220        1.1975             nan     0.0500   -0.0032
##    240        1.0984             nan     0.0500   -0.0135
##    260        1.0196             nan     0.0500   -0.0085
##    280        0.9531             nan     0.0500   -0.0084
##    300        0.8882             nan     0.0500   -0.0063
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.5982             nan     0.0500    5.1419
##      2       52.7043             nan     0.0500    5.2285
##      3       48.2017             nan     0.0500    4.5196
##      4       44.0392             nan     0.0500    4.2655
##      5       40.4676             nan     0.0500    3.4111
##      6       37.1336             nan     0.0500    3.1229
##      7       34.1011             nan     0.0500    3.1204
##      8       31.2700             nan     0.0500    2.4294
##      9       28.8297             nan     0.0500    2.3109
##     10       26.6413             nan     0.0500    1.9269
##     20       12.5885             nan     0.0500    0.8037
##     40        4.6634             nan     0.0500    0.1170
##     60        3.2042             nan     0.0500    0.0020
##     80        2.7327             nan     0.0500   -0.0121
##    100        2.4669             nan     0.0500   -0.0063
##    120        2.2736             nan     0.0500   -0.0336
##    140        2.1018             nan     0.0500   -0.0174
##    160        1.9830             nan     0.0500   -0.0081
##    180        1.8582             nan     0.0500   -0.0162
##    200        1.7204             nan     0.0500   -0.0102
##    220        1.6135             nan     0.0500   -0.0182
##    240        1.5285             nan     0.0500   -0.0076
##    260        1.4476             nan     0.0500   -0.0189
##    280        1.3701             nan     0.0500   -0.0123
##    300        1.3030             nan     0.0500   -0.0074
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.8167             nan     0.0500    5.4355
##      2       52.9151             nan     0.0500    4.9638
##      3       48.5077             nan     0.0500    4.4417
##      4       44.4762             nan     0.0500    4.2603
##      5       40.9281             nan     0.0500    3.1023
##      6       37.9542             nan     0.0500    3.2136
##      7       34.8536             nan     0.0500    3.1168
##      8       32.0196             nan     0.0500    2.7030
##      9       29.3508             nan     0.0500    2.3860
##     10       27.1544             nan     0.0500    2.1126
##     20       13.3726             nan     0.0500    0.8988
##     40        5.1666             nan     0.0500    0.1075
##     60        3.5493             nan     0.0500   -0.0049
##     80        3.0793             nan     0.0500   -0.0079
##    100        2.8443             nan     0.0500   -0.0295
##    120        2.6534             nan     0.0500   -0.0263
##    140        2.4687             nan     0.0500   -0.0089
##    160        2.3371             nan     0.0500   -0.0117
##    180        2.2094             nan     0.0500   -0.0177
##    200        2.0858             nan     0.0500   -0.0164
##    220        1.9686             nan     0.0500   -0.0107
##    240        1.8652             nan     0.0500   -0.0170
##    260        1.7810             nan     0.0500   -0.0092
##    280        1.7019             nan     0.0500   -0.0055
##    300        1.6366             nan     0.0500   -0.0249
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.8709             nan     0.1000    7.4844
##      2       48.8909             nan     0.1000    7.1654
##      3       43.3184             nan     0.1000    5.2283
##      4       38.6295             nan     0.1000    4.1805
##      5       34.9287             nan     0.1000    3.4725
##      6       31.7173             nan     0.1000    2.5913
##      7       29.3212             nan     0.1000    2.0871
##      8       26.7942             nan     0.1000    2.3069
##      9       24.3090             nan     0.1000    2.2137
##     10       22.3158             nan     0.1000    1.4802
##     20       11.1699             nan     0.1000    0.7516
##     40        5.3493             nan     0.1000    0.0571
##     60        4.1689             nan     0.1000    0.0173
##     80        3.7641             nan     0.1000   -0.0021
##    100        3.5711             nan     0.1000   -0.0369
##    120        3.4760             nan     0.1000   -0.0062
##    140        3.3479             nan     0.1000   -0.0188
##    160        3.2690             nan     0.1000   -0.0184
##    180        3.1594             nan     0.1000   -0.0287
##    200        3.1050             nan     0.1000   -0.0311
##    220        3.0130             nan     0.1000   -0.0134
##    240        2.9732             nan     0.1000   -0.0111
##    260        2.9131             nan     0.1000   -0.0225
##    280        2.8605             nan     0.1000   -0.0082
##    300        2.8109             nan     0.1000   -0.0155
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.0976             nan     0.1000    7.6380
##      2       48.9094             nan     0.1000    6.1164
##      3       43.9099             nan     0.1000    5.2759
##      4       38.8845             nan     0.1000    5.1913
##      5       35.0466             nan     0.1000    3.4813
##      6       31.1977             nan     0.1000    3.8546
##      7       28.5896             nan     0.1000    1.9545
##      8       25.9762             nan     0.1000    2.2206
##      9       23.6819             nan     0.1000    2.2322
##     10       21.8832             nan     0.1000    1.7804
##     20       11.1010             nan     0.1000    0.5533
##     40        5.4764             nan     0.1000    0.0533
##     60        4.1226             nan     0.1000    0.0117
##     80        3.8003             nan     0.1000    0.0030
##    100        3.6072             nan     0.1000   -0.0292
##    120        3.4601             nan     0.1000   -0.0065
##    140        3.3756             nan     0.1000   -0.0112
##    160        3.3047             nan     0.1000   -0.0079
##    180        3.2359             nan     0.1000   -0.0255
##    200        3.1807             nan     0.1000   -0.0178
##    220        3.1151             nan     0.1000   -0.0087
##    240        3.0685             nan     0.1000   -0.0178
##    260        3.0173             nan     0.1000   -0.0198
##    280        2.9703             nan     0.1000   -0.0207
##    300        2.9332             nan     0.1000   -0.0272
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.0434             nan     0.1000    6.9573
##      2       48.9979             nan     0.1000    6.3344
##      3       43.4028             nan     0.1000    5.1258
##      4       39.5284             nan     0.1000    3.9916
##      5       35.5220             nan     0.1000    3.7960
##      6       32.4224             nan     0.1000    2.6873
##      7       29.2578             nan     0.1000    3.7091
##      8       26.1247             nan     0.1000    2.6075
##      9       23.9631             nan     0.1000    1.9019
##     10       22.1365             nan     0.1000    1.9129
##     20       11.0655             nan     0.1000    0.4904
##     40        5.5943             nan     0.1000    0.1175
##     60        4.4320             nan     0.1000   -0.0231
##     80        4.1626             nan     0.1000    0.0019
##    100        3.9434             nan     0.1000   -0.0215
##    120        3.7856             nan     0.1000   -0.0157
##    140        3.6685             nan     0.1000   -0.0130
##    160        3.5767             nan     0.1000   -0.0338
##    180        3.4809             nan     0.1000   -0.0247
##    200        3.4153             nan     0.1000   -0.0260
##    220        3.3637             nan     0.1000   -0.0184
##    240        3.2997             nan     0.1000   -0.0220
##    260        3.2196             nan     0.1000   -0.0037
##    280        3.1752             nan     0.1000   -0.0276
##    300        3.1396             nan     0.1000   -0.0097
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.1825             nan     0.1000    8.8474
##      2       45.3544             nan     0.1000    8.0821
##      3       38.3531             nan     0.1000    6.8722
##      4       32.8831             nan     0.1000    5.5796
##      5       28.0763             nan     0.1000    4.7544
##      6       23.9805             nan     0.1000    4.0557
##      7       20.7236             nan     0.1000    2.8624
##      8       18.0459             nan     0.1000    2.3807
##      9       15.7382             nan     0.1000    2.2804
##     10       13.6443             nan     0.1000    1.9852
##     20        5.6796             nan     0.1000    0.2747
##     40        3.1328             nan     0.1000   -0.0065
##     60        2.6449             nan     0.1000   -0.0377
##     80        2.2908             nan     0.1000   -0.0236
##    100        2.0302             nan     0.1000   -0.0200
##    120        1.8393             nan     0.1000   -0.0223
##    140        1.6754             nan     0.1000   -0.0144
##    160        1.5510             nan     0.1000   -0.0144
##    180        1.4331             nan     0.1000   -0.0157
##    200        1.3177             nan     0.1000   -0.0295
##    220        1.2008             nan     0.1000   -0.0218
##    240        1.1064             nan     0.1000   -0.0262
##    260        1.0342             nan     0.1000   -0.0147
##    280        0.9668             nan     0.1000   -0.0105
##    300        0.9057             nan     0.1000   -0.0095
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.5868             nan     0.1000    9.8999
##      2       45.1692             nan     0.1000    8.3824
##      3       38.1811             nan     0.1000    7.8841
##      4       32.2389             nan     0.1000    6.2928
##      5       27.7172             nan     0.1000    4.2075
##      6       23.9832             nan     0.1000    3.9251
##      7       20.5821             nan     0.1000    2.7446
##      8       18.3428             nan     0.1000    2.7741
##      9       15.9871             nan     0.1000    2.0383
##     10       14.1993             nan     0.1000    1.6286
##     20        5.8069             nan     0.1000    0.3173
##     40        3.3438             nan     0.1000   -0.0011
##     60        2.8205             nan     0.1000   -0.0395
##     80        2.5355             nan     0.1000   -0.0163
##    100        2.3143             nan     0.1000    0.0067
##    120        2.1039             nan     0.1000   -0.0054
##    140        1.9622             nan     0.1000   -0.0239
##    160        1.8067             nan     0.1000   -0.0193
##    180        1.7008             nan     0.1000   -0.0135
##    200        1.5884             nan     0.1000   -0.0142
##    220        1.5108             nan     0.1000   -0.0246
##    240        1.4212             nan     0.1000   -0.0177
##    260        1.3480             nan     0.1000   -0.0240
##    280        1.2914             nan     0.1000   -0.0068
##    300        1.2170             nan     0.1000   -0.0182
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.0748             nan     0.1000   10.6753
##      2       44.7872             nan     0.1000    8.1787
##      3       38.2032             nan     0.1000    6.4469
##      4       32.6412             nan     0.1000    5.0470
##      5       27.9569             nan     0.1000    4.3828
##      6       24.0892             nan     0.1000    4.1616
##      7       20.7450             nan     0.1000    3.2831
##      8       18.3223             nan     0.1000    2.6124
##      9       16.1706             nan     0.1000    2.0841
##     10       14.3961             nan     0.1000    1.6339
##     20        6.1383             nan     0.1000    0.1825
##     40        3.5999             nan     0.1000   -0.0380
##     60        3.1642             nan     0.1000   -0.0276
##     80        2.8800             nan     0.1000   -0.0243
##    100        2.6763             nan     0.1000   -0.0254
##    120        2.4645             nan     0.1000   -0.0148
##    140        2.3007             nan     0.1000   -0.0425
##    160        2.1856             nan     0.1000   -0.0465
##    180        2.0540             nan     0.1000   -0.0054
##    200        1.9323             nan     0.1000   -0.0259
##    220        1.8353             nan     0.1000   -0.0195
##    240        1.7186             nan     0.1000   -0.0106
##    260        1.6306             nan     0.1000   -0.0263
##    280        1.5604             nan     0.1000   -0.0124
##    300        1.4810             nan     0.1000   -0.0175
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.5608             nan     0.1000   10.3111
##      2       43.9035             nan     0.1000    9.1471
##      3       36.7392             nan     0.1000    7.2499
##      4       31.0089             nan     0.1000    5.8657
##      5       26.3105             nan     0.1000    4.5241
##      6       22.4058             nan     0.1000    3.9436
##      7       19.1915             nan     0.1000    3.0491
##      8       16.4741             nan     0.1000    2.5795
##      9       14.2474             nan     0.1000    2.2784
##     10       12.5049             nan     0.1000    1.7786
##     20        4.6624             nan     0.1000    0.2582
##     40        2.5818             nan     0.1000   -0.0066
##     60        1.9679             nan     0.1000   -0.0325
##     80        1.6002             nan     0.1000   -0.0226
##    100        1.3540             nan     0.1000   -0.0161
##    120        1.1498             nan     0.1000   -0.0136
##    140        0.9919             nan     0.1000   -0.0267
##    160        0.8499             nan     0.1000   -0.0157
##    180        0.7349             nan     0.1000   -0.0061
##    200        0.6403             nan     0.1000   -0.0099
##    220        0.5614             nan     0.1000   -0.0153
##    240        0.5055             nan     0.1000   -0.0072
##    260        0.4534             nan     0.1000   -0.0073
##    280        0.4083             nan     0.1000   -0.0065
##    300        0.3733             nan     0.1000   -0.0071
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.3284             nan     0.1000   10.0724
##      2       43.5374             nan     0.1000    8.9578
##      3       36.9045             nan     0.1000    6.5754
##      4       31.3420             nan     0.1000    5.9663
##      5       26.6040             nan     0.1000    4.1599
##      6       22.5944             nan     0.1000    3.6266
##      7       19.2239             nan     0.1000    3.1525
##      8       16.4007             nan     0.1000    3.0384
##      9       14.1583             nan     0.1000    2.1247
##     10       12.3126             nan     0.1000    1.8650
##     20        4.5941             nan     0.1000    0.2321
##     40        2.6499             nan     0.1000   -0.0497
##     60        2.2690             nan     0.1000   -0.0326
##     80        1.9554             nan     0.1000   -0.0047
##    100        1.7331             nan     0.1000   -0.0390
##    120        1.5406             nan     0.1000   -0.0152
##    140        1.3897             nan     0.1000   -0.0130
##    160        1.2579             nan     0.1000   -0.0224
##    180        1.1366             nan     0.1000   -0.0260
##    200        1.0442             nan     0.1000   -0.0227
##    220        0.9432             nan     0.1000   -0.0127
##    240        0.8643             nan     0.1000   -0.0111
##    260        0.7949             nan     0.1000   -0.0077
##    280        0.7388             nan     0.1000   -0.0090
##    300        0.6876             nan     0.1000   -0.0085
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.8004             nan     0.1000   10.4799
##      2       43.9093             nan     0.1000    8.7984
##      3       36.8988             nan     0.1000    7.5465
##      4       31.4247             nan     0.1000    5.5004
##      5       26.8221             nan     0.1000    4.8386
##      6       22.7944             nan     0.1000    3.6298
##      7       19.6499             nan     0.1000    2.9841
##      8       17.0872             nan     0.1000    2.5629
##      9       15.0596             nan     0.1000    1.7730
##     10       13.2765             nan     0.1000    1.8008
##     20        5.2457             nan     0.1000    0.2893
##     40        3.1964             nan     0.1000   -0.0233
##     60        2.7338             nan     0.1000   -0.0321
##     80        2.4244             nan     0.1000   -0.0454
##    100        2.1530             nan     0.1000   -0.0122
##    120        1.9598             nan     0.1000   -0.0248
##    140        1.7813             nan     0.1000   -0.0071
##    160        1.6445             nan     0.1000   -0.0306
##    180        1.5270             nan     0.1000   -0.0250
##    200        1.4495             nan     0.1000   -0.0274
##    220        1.3159             nan     0.1000   -0.0140
##    240        1.2103             nan     0.1000   -0.0149
##    260        1.1202             nan     0.1000   -0.0155
##    280        1.0456             nan     0.1000   -0.0115
##    300        0.9960             nan     0.1000   -0.0219
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.9368             nan     0.0100    0.6958
##      2       58.2282             nan     0.0100    0.6959
##      3       57.5163             nan     0.0100    0.6871
##      4       56.8017             nan     0.0100    0.7084
##      5       56.0913             nan     0.0100    0.6493
##      6       55.3699             nan     0.0100    0.6966
##      7       54.7054             nan     0.0100    0.7111
##      8       54.0419             nan     0.0100    0.7034
##      9       53.4059             nan     0.0100    0.6037
##     10       52.7683             nan     0.0100    0.5773
##     20       46.7715             nan     0.0100    0.4481
##     40       37.7370             nan     0.0100    0.3473
##     60       30.6736             nan     0.0100    0.2820
##     80       25.3878             nan     0.0100    0.2176
##    100       21.3613             nan     0.0100    0.1826
##    120       18.2616             nan     0.0100    0.1124
##    140       15.8383             nan     0.0100    0.0775
##    160       13.8920             nan     0.0100    0.0826
##    180       12.3446             nan     0.0100    0.0589
##    200       11.0718             nan     0.0100    0.0580
##    220        9.9955             nan     0.0100    0.0406
##    240        9.0836             nan     0.0100    0.0363
##    260        8.3120             nan     0.0100    0.0118
##    280        7.6393             nan     0.0100    0.0271
##    300        7.0795             nan     0.0100    0.0251
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.9345             nan     0.0100    0.7421
##      2       58.1976             nan     0.0100    0.7083
##      3       57.4544             nan     0.0100    0.7288
##      4       56.7585             nan     0.0100    0.7630
##      5       56.0923             nan     0.0100    0.6877
##      6       55.3795             nan     0.0100    0.6640
##      7       54.7033             nan     0.0100    0.6739
##      8       54.0282             nan     0.0100    0.6526
##      9       53.4135             nan     0.0100    0.6435
##     10       52.7848             nan     0.0100    0.6709
##     20       47.0669             nan     0.0100    0.5421
##     40       38.0184             nan     0.0100    0.4092
##     60       30.9632             nan     0.0100    0.2853
##     80       25.7020             nan     0.0100    0.2138
##    100       21.6669             nan     0.0100    0.1600
##    120       18.4841             nan     0.0100    0.1230
##    140       16.0269             nan     0.0100    0.0938
##    160       14.0636             nan     0.0100    0.0904
##    180       12.4889             nan     0.0100    0.0623
##    200       11.1804             nan     0.0100    0.0535
##    220       10.0825             nan     0.0100    0.0487
##    240        9.1349             nan     0.0100    0.0537
##    260        8.3593             nan     0.0100    0.0275
##    280        7.7144             nan     0.0100    0.0223
##    300        7.1423             nan     0.0100    0.0184
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.8447             nan     0.0100    0.7519
##      2       58.1389             nan     0.0100    0.7079
##      3       57.4214             nan     0.0100    0.7265
##      4       56.6957             nan     0.0100    0.7188
##      5       55.9591             nan     0.0100    0.7129
##      6       55.2185             nan     0.0100    0.7421
##      7       54.5820             nan     0.0100    0.6987
##      8       53.9502             nan     0.0100    0.6532
##      9       53.2727             nan     0.0100    0.6037
##     10       52.7122             nan     0.0100    0.5277
##     20       47.0099             nan     0.0100    0.5173
##     40       37.9019             nan     0.0100    0.3892
##     60       31.0371             nan     0.0100    0.2787
##     80       25.7268             nan     0.0100    0.2188
##    100       21.6772             nan     0.0100    0.1497
##    120       18.5327             nan     0.0100    0.1388
##    140       16.1403             nan     0.0100    0.0981
##    160       14.1824             nan     0.0100    0.0732
##    180       12.5550             nan     0.0100    0.0619
##    200       11.1649             nan     0.0100    0.0580
##    220       10.0838             nan     0.0100    0.0428
##    240        9.1792             nan     0.0100    0.0310
##    260        8.4321             nan     0.0100    0.0224
##    280        7.7832             nan     0.0100    0.0271
##    300        7.2327             nan     0.0100    0.0193
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.6534             nan     0.0100    1.0062
##      2       57.6516             nan     0.0100    0.9429
##      3       56.7025             nan     0.0100    0.9350
##      4       55.7856             nan     0.0100    0.9256
##      5       54.9393             nan     0.0100    0.9323
##      6       54.0453             nan     0.0100    0.8522
##      7       53.2068             nan     0.0100    0.9016
##      8       52.3138             nan     0.0100    0.8958
##      9       51.5176             nan     0.0100    0.7822
##     10       50.7209             nan     0.0100    0.8833
##     20       43.2229             nan     0.0100    0.6959
##     40       31.5793             nan     0.0100    0.4079
##     60       23.5262             nan     0.0100    0.3021
##     80       18.0266             nan     0.0100    0.1920
##    100       14.1394             nan     0.0100    0.1813
##    120       11.3292             nan     0.0100    0.1141
##    140        9.2786             nan     0.0100    0.0624
##    160        7.7394             nan     0.0100    0.0552
##    180        6.6288             nan     0.0100    0.0350
##    200        5.7798             nan     0.0100    0.0308
##    220        5.1309             nan     0.0100    0.0164
##    240        4.6390             nan     0.0100    0.0177
##    260        4.2494             nan     0.0100    0.0089
##    280        3.9517             nan     0.0100    0.0077
##    300        3.7216             nan     0.0100    0.0054
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.6537             nan     0.0100    0.9949
##      2       57.7285             nan     0.0100    0.8895
##      3       56.7543             nan     0.0100    0.9134
##      4       55.8228             nan     0.0100    1.0075
##      5       54.8791             nan     0.0100    0.8516
##      6       53.9636             nan     0.0100    0.8997
##      7       53.0311             nan     0.0100    0.9704
##      8       52.1466             nan     0.0100    0.9019
##      9       51.2903             nan     0.0100    0.7765
##     10       50.4514             nan     0.0100    0.8287
##     20       42.9765             nan     0.0100    0.6812
##     40       31.4786             nan     0.0100    0.5159
##     60       23.5451             nan     0.0100    0.3427
##     80       17.9372             nan     0.0100    0.1988
##    100       14.0338             nan     0.0100    0.1616
##    120       11.3038             nan     0.0100    0.1158
##    140        9.2408             nan     0.0100    0.0710
##    160        7.7725             nan     0.0100    0.0555
##    180        6.6545             nan     0.0100    0.0426
##    200        5.7857             nan     0.0100    0.0283
##    220        5.1513             nan     0.0100    0.0212
##    240        4.6650             nan     0.0100    0.0137
##    260        4.3122             nan     0.0100    0.0116
##    280        4.0165             nan     0.0100    0.0088
##    300        3.8111             nan     0.0100    0.0060
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.6552             nan     0.0100    0.9249
##      2       57.6478             nan     0.0100    1.0111
##      3       56.6581             nan     0.0100    1.0011
##      4       55.7095             nan     0.0100    0.9740
##      5       54.7816             nan     0.0100    0.9540
##      6       53.8327             nan     0.0100    1.0074
##      7       52.9336             nan     0.0100    0.8186
##      8       52.0684             nan     0.0100    0.7978
##      9       51.2438             nan     0.0100    0.8293
##     10       50.4099             nan     0.0100    0.7810
##     20       42.9934             nan     0.0100    0.6995
##     40       31.6879             nan     0.0100    0.4722
##     60       23.7871             nan     0.0100    0.3308
##     80       18.2452             nan     0.0100    0.2423
##    100       14.4221             nan     0.0100    0.1412
##    120       11.6408             nan     0.0100    0.0893
##    140        9.6140             nan     0.0100    0.0632
##    160        8.1201             nan     0.0100    0.0480
##    180        6.9933             nan     0.0100    0.0473
##    200        6.1425             nan     0.0100    0.0244
##    220        5.4989             nan     0.0100    0.0263
##    240        5.0032             nan     0.0100    0.0205
##    260        4.6434             nan     0.0100    0.0101
##    280        4.3591             nan     0.0100    0.0067
##    300        4.1404             nan     0.0100    0.0028
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.6215             nan     0.0100    0.9360
##      2       57.5973             nan     0.0100    1.0649
##      3       56.5718             nan     0.0100    0.9783
##      4       55.6346             nan     0.0100    0.9597
##      5       54.6489             nan     0.0100    0.9505
##      6       53.6826             nan     0.0100    0.8583
##      7       52.7759             nan     0.0100    0.8184
##      8       51.8215             nan     0.0100    0.8624
##      9       50.8656             nan     0.0100    0.9234
##     10       49.9881             nan     0.0100    0.8717
##     20       42.0980             nan     0.0100    0.7069
##     40       30.0272             nan     0.0100    0.4499
##     60       21.8002             nan     0.0100    0.3024
##     80       16.3335             nan     0.0100    0.1964
##    100       12.4237             nan     0.0100    0.1491
##    120        9.7270             nan     0.0100    0.1098
##    140        7.8229             nan     0.0100    0.0581
##    160        6.4332             nan     0.0100    0.0460
##    180        5.4406             nan     0.0100    0.0214
##    200        4.7048             nan     0.0100    0.0166
##    220        4.1639             nan     0.0100    0.0161
##    240        3.7474             nan     0.0100    0.0091
##    260        3.4471             nan     0.0100    0.0060
##    280        3.2346             nan     0.0100   -0.0014
##    300        3.0543             nan     0.0100    0.0030
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.6047             nan     0.0100    0.9812
##      2       57.6138             nan     0.0100    1.0773
##      3       56.5962             nan     0.0100    0.9424
##      4       55.5893             nan     0.0100    0.8490
##      5       54.5629             nan     0.0100    0.8462
##      6       53.5969             nan     0.0100    0.9303
##      7       52.6481             nan     0.0100    0.8918
##      8       51.7491             nan     0.0100    0.9577
##      9       50.8389             nan     0.0100    0.9734
##     10       49.9636             nan     0.0100    0.8165
##     20       42.1400             nan     0.0100    0.6951
##     40       30.2455             nan     0.0100    0.4716
##     60       22.0958             nan     0.0100    0.3687
##     80       16.4790             nan     0.0100    0.2389
##    100       12.4753             nan     0.0100    0.1586
##    120        9.7095             nan     0.0100    0.1091
##    140        7.7975             nan     0.0100    0.0659
##    160        6.4313             nan     0.0100    0.0449
##    180        5.4768             nan     0.0100    0.0254
##    200        4.7724             nan     0.0100    0.0253
##    220        4.2325             nan     0.0100    0.0134
##    240        3.8623             nan     0.0100    0.0124
##    260        3.5654             nan     0.0100    0.0033
##    280        3.3369             nan     0.0100    0.0006
##    300        3.1697             nan     0.0100    0.0020
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.6271             nan     0.0100    0.8861
##      2       57.5992             nan     0.0100    1.0159
##      3       56.5539             nan     0.0100    1.1194
##      4       55.6044             nan     0.0100    0.9860
##      5       54.6918             nan     0.0100    0.8781
##      6       53.7361             nan     0.0100    0.9336
##      7       52.8073             nan     0.0100    0.7909
##      8       51.9111             nan     0.0100    0.7630
##      9       51.0111             nan     0.0100    0.8385
##     10       50.1078             nan     0.0100    0.9061
##     20       42.2308             nan     0.0100    0.6613
##     40       30.3481             nan     0.0100    0.4266
##     60       22.1665             nan     0.0100    0.3193
##     80       16.5886             nan     0.0100    0.2666
##    100       12.8261             nan     0.0100    0.1298
##    120       10.1455             nan     0.0100    0.0981
##    140        8.2573             nan     0.0100    0.0791
##    160        6.9054             nan     0.0100    0.0444
##    180        5.9115             nan     0.0100    0.0329
##    200        5.1973             nan     0.0100    0.0261
##    220        4.6745             nan     0.0100    0.0171
##    240        4.2928             nan     0.0100    0.0069
##    260        4.0051             nan     0.0100    0.0054
##    280        3.7803             nan     0.0100    0.0014
##    300        3.6225             nan     0.0100    0.0025
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.7455             nan     0.0500    3.9885
##      2       52.3909             nan     0.0500    3.4541
##      3       49.6441             nan     0.0500    2.4246
##      4       46.8784             nan     0.0500    2.8210
##      5       44.2155             nan     0.0500    2.5158
##      6       41.7622             nan     0.0500    2.2067
##      7       39.6512             nan     0.0500    2.1829
##      8       37.6377             nan     0.0500    1.6979
##      9       35.6221             nan     0.0500    2.1787
##     10       33.7747             nan     0.0500    1.6337
##     20       21.4593             nan     0.0500    0.8620
##     40       11.1655             nan     0.0500    0.2403
##     60        6.9941             nan     0.0500    0.1245
##     80        5.2472             nan     0.0500    0.0057
##    100        4.3751             nan     0.0500    0.0243
##    120        3.9301             nan     0.0500   -0.0243
##    140        3.7095             nan     0.0500   -0.0018
##    160        3.6022             nan     0.0500    0.0036
##    180        3.5058             nan     0.0500   -0.0025
##    200        3.4432             nan     0.0500   -0.0149
##    220        3.3800             nan     0.0500   -0.0012
##    240        3.3194             nan     0.0500   -0.0089
##    260        3.2774             nan     0.0500   -0.0240
##    280        3.2239             nan     0.0500   -0.0091
##    300        3.1706             nan     0.0500    0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.1470             nan     0.0500    3.6240
##      2       52.7097             nan     0.0500    3.6194
##      3       49.8643             nan     0.0500    2.3946
##      4       46.7450             nan     0.0500    2.8934
##      5       44.2464             nan     0.0500    2.4908
##      6       41.6984             nan     0.0500    2.2906
##      7       39.5403             nan     0.0500    2.3850
##      8       37.4274             nan     0.0500    2.0538
##      9       35.4990             nan     0.0500    1.8143
##     10       33.5412             nan     0.0500    1.7861
##     20       21.2077             nan     0.0500    0.7627
##     40       10.8542             nan     0.0500    0.2377
##     60        7.0048             nan     0.0500    0.0641
##     80        5.2486             nan     0.0500    0.0531
##    100        4.4565             nan     0.0500    0.0244
##    120        4.0000             nan     0.0500    0.0092
##    140        3.8053             nan     0.0500   -0.0192
##    160        3.6794             nan     0.0500   -0.0194
##    180        3.5938             nan     0.0500   -0.0015
##    200        3.5247             nan     0.0500   -0.0008
##    220        3.4575             nan     0.0500   -0.0005
##    240        3.3980             nan     0.0500   -0.0083
##    260        3.3367             nan     0.0500   -0.0058
##    280        3.2924             nan     0.0500   -0.0107
##    300        3.2441             nan     0.0500   -0.0063
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.8003             nan     0.0500    3.6979
##      2       52.4842             nan     0.0500    3.2202
##      3       49.3210             nan     0.0500    2.9657
##      4       46.8059             nan     0.0500    2.1259
##      5       44.2487             nan     0.0500    2.7197
##      6       41.7047             nan     0.0500    2.2589
##      7       39.4682             nan     0.0500    2.1230
##      8       37.2456             nan     0.0500    2.1317
##      9       35.4520             nan     0.0500    1.9088
##     10       33.7465             nan     0.0500    1.6150
##     20       21.8730             nan     0.0500    0.9580
##     40       11.1305             nan     0.0500    0.3069
##     60        7.1215             nan     0.0500    0.1033
##     80        5.3705             nan     0.0500    0.0294
##    100        4.5417             nan     0.0500    0.0160
##    120        4.1591             nan     0.0500   -0.0060
##    140        3.9792             nan     0.0500    0.0003
##    160        3.8654             nan     0.0500   -0.0049
##    180        3.7677             nan     0.0500   -0.0092
##    200        3.6709             nan     0.0500   -0.0112
##    220        3.5893             nan     0.0500   -0.0045
##    240        3.5239             nan     0.0500   -0.0071
##    260        3.4592             nan     0.0500   -0.0048
##    280        3.4050             nan     0.0500   -0.0089
##    300        3.3521             nan     0.0500   -0.0084
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.6431             nan     0.0500    4.9729
##      2       50.3649             nan     0.0500    4.1650
##      3       46.4633             nan     0.0500    3.9962
##      4       42.6439             nan     0.0500    3.4716
##      5       39.1465             nan     0.0500    3.0613
##      6       36.1308             nan     0.0500    3.1025
##      7       33.2877             nan     0.0500    2.2693
##      8       30.8152             nan     0.0500    2.6181
##      9       28.6140             nan     0.0500    1.9878
##     10       26.5724             nan     0.0500    1.9819
##     20       13.7432             nan     0.0500    0.9163
##     40        5.7383             nan     0.0500    0.1554
##     60        3.7928             nan     0.0500    0.0238
##     80        3.1485             nan     0.0500   -0.0034
##    100        2.8603             nan     0.0500   -0.0127
##    120        2.6383             nan     0.0500   -0.0008
##    140        2.4793             nan     0.0500   -0.0328
##    160        2.3070             nan     0.0500   -0.0274
##    180        2.1608             nan     0.0500   -0.0084
##    200        2.0373             nan     0.0500   -0.0014
##    220        1.9326             nan     0.0500   -0.0114
##    240        1.8379             nan     0.0500   -0.0115
##    260        1.7367             nan     0.0500   -0.0045
##    280        1.6568             nan     0.0500   -0.0048
##    300        1.5973             nan     0.0500   -0.0081
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.0196             nan     0.0500    4.6119
##      2       50.8345             nan     0.0500    4.1510
##      3       46.7612             nan     0.0500    3.7373
##      4       43.0339             nan     0.0500    3.1780
##      5       39.8748             nan     0.0500    3.0077
##      6       36.6364             nan     0.0500    2.9471
##      7       33.8056             nan     0.0500    2.5013
##      8       31.2879             nan     0.0500    2.3027
##      9       28.9631             nan     0.0500    1.8076
##     10       26.9609             nan     0.0500    2.0095
##     20       13.8211             nan     0.0500    0.8349
##     40        5.8828             nan     0.0500    0.0954
##     60        3.8975             nan     0.0500    0.0086
##     80        3.3065             nan     0.0500   -0.0112
##    100        3.0233             nan     0.0500   -0.0038
##    120        2.8387             nan     0.0500   -0.0094
##    140        2.6716             nan     0.0500   -0.0180
##    160        2.5129             nan     0.0500   -0.0338
##    180        2.4064             nan     0.0500   -0.0118
##    200        2.2939             nan     0.0500   -0.0132
##    220        2.1910             nan     0.0500   -0.0083
##    240        2.1030             nan     0.0500   -0.0186
##    260        2.0391             nan     0.0500   -0.0136
##    280        1.9786             nan     0.0500   -0.0257
##    300        1.9144             nan     0.0500   -0.0086
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.6565             nan     0.0500    4.9930
##      2       50.0634             nan     0.0500    4.2518
##      3       45.9514             nan     0.0500    3.4755
##      4       42.2227             nan     0.0500    3.5463
##      5       38.9535             nan     0.0500    2.9727
##      6       36.0928             nan     0.0500    3.0717
##      7       33.4569             nan     0.0500    2.7614
##      8       30.8881             nan     0.0500    2.3115
##      9       28.7637             nan     0.0500    2.1251
##     10       26.7774             nan     0.0500    2.0122
##     20       14.4285             nan     0.0500    0.8656
##     40        6.1414             nan     0.0500    0.1275
##     60        4.1800             nan     0.0500    0.0202
##     80        3.6274             nan     0.0500   -0.0161
##    100        3.3072             nan     0.0500   -0.0053
##    120        3.1024             nan     0.0500   -0.0137
##    140        2.9503             nan     0.0500   -0.0127
##    160        2.8079             nan     0.0500   -0.0123
##    180        2.6939             nan     0.0500   -0.0130
##    200        2.5996             nan     0.0500   -0.0085
##    220        2.5037             nan     0.0500   -0.0212
##    240        2.4287             nan     0.0500   -0.0105
##    260        2.3524             nan     0.0500   -0.0111
##    280        2.2838             nan     0.0500   -0.0182
##    300        2.2256             nan     0.0500   -0.0294
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.6937             nan     0.0500    4.5151
##      2       50.4994             nan     0.0500    4.4749
##      3       46.0594             nan     0.0500    4.6181
##      4       42.2246             nan     0.0500    3.7210
##      5       38.6145             nan     0.0500    3.3517
##      6       35.4594             nan     0.0500    3.0589
##      7       32.5716             nan     0.0500    3.0667
##      8       30.0517             nan     0.0500    2.5425
##      9       27.8618             nan     0.0500    2.2512
##     10       25.6538             nan     0.0500    2.1904
##     20       12.3599             nan     0.0500    0.9173
##     40        4.6041             nan     0.0500    0.0833
##     60        2.9897             nan     0.0500   -0.0045
##     80        2.4872             nan     0.0500   -0.0106
##    100        2.1966             nan     0.0500   -0.0273
##    120        1.9864             nan     0.0500   -0.0176
##    140        1.7963             nan     0.0500   -0.0179
##    160        1.6543             nan     0.0500   -0.0153
##    180        1.5219             nan     0.0500   -0.0010
##    200        1.3963             nan     0.0500   -0.0133
##    220        1.2837             nan     0.0500   -0.0061
##    240        1.1931             nan     0.0500   -0.0131
##    260        1.1063             nan     0.0500   -0.0039
##    280        1.0308             nan     0.0500   -0.0109
##    300        0.9593             nan     0.0500   -0.0099
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.3349             nan     0.0500    4.6946
##      2       49.6219             nan     0.0500    5.1064
##      3       45.5924             nan     0.0500    3.8907
##      4       41.9220             nan     0.0500    3.3193
##      5       38.6614             nan     0.0500    3.4101
##      6       35.5781             nan     0.0500    3.2242
##      7       32.5910             nan     0.0500    3.0844
##      8       30.0082             nan     0.0500    2.4565
##      9       27.6492             nan     0.0500    2.1421
##     10       25.5429             nan     0.0500    2.2653
##     20       12.1288             nan     0.0500    0.7503
##     40        4.6470             nan     0.0500    0.1056
##     60        3.1391             nan     0.0500    0.0011
##     80        2.6168             nan     0.0500   -0.0160
##    100        2.3787             nan     0.0500   -0.0212
##    120        2.1827             nan     0.0500   -0.0093
##    140        2.0216             nan     0.0500   -0.0102
##    160        1.8625             nan     0.0500   -0.0148
##    180        1.7366             nan     0.0500   -0.0176
##    200        1.6237             nan     0.0500   -0.0088
##    220        1.5395             nan     0.0500   -0.0069
##    240        1.4601             nan     0.0500   -0.0049
##    260        1.3888             nan     0.0500   -0.0097
##    280        1.3241             nan     0.0500   -0.0140
##    300        1.2637             nan     0.0500   -0.0151
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.5929             nan     0.0500    5.4908
##      2       49.9664             nan     0.0500    3.9270
##      3       45.7726             nan     0.0500    4.1623
##      4       41.9654             nan     0.0500    3.7929
##      5       38.6443             nan     0.0500    3.4393
##      6       35.4581             nan     0.0500    3.0428
##      7       32.7175             nan     0.0500    2.7409
##      8       30.2537             nan     0.0500    2.5740
##      9       28.0862             nan     0.0500    2.2820
##     10       26.0135             nan     0.0500    1.9770
##     20       12.5471             nan     0.0500    0.8101
##     40        5.1348             nan     0.0500    0.1186
##     60        3.6932             nan     0.0500    0.0164
##     80        3.2272             nan     0.0500   -0.0119
##    100        2.9629             nan     0.0500   -0.0061
##    120        2.7384             nan     0.0500   -0.0190
##    140        2.5361             nan     0.0500   -0.0130
##    160        2.3896             nan     0.0500   -0.0201
##    180        2.2547             nan     0.0500   -0.0141
##    200        2.1351             nan     0.0500   -0.0123
##    220        2.0283             nan     0.0500   -0.0114
##    240        1.9462             nan     0.0500   -0.0106
##    260        1.8627             nan     0.0500   -0.0144
##    280        1.7964             nan     0.0500   -0.0110
##    300        1.7311             nan     0.0500   -0.0090
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.9839             nan     0.1000    6.9754
##      2       47.1768             nan     0.1000    5.6638
##      3       42.1405             nan     0.1000    4.9206
##      4       37.9827             nan     0.1000    4.2613
##      5       33.9113             nan     0.1000    3.1185
##      6       30.4651             nan     0.1000    3.3412
##      7       27.7503             nan     0.1000    2.7547
##      8       24.9550             nan     0.1000    2.5442
##      9       22.8597             nan     0.1000    2.0980
##     10       20.8382             nan     0.1000    1.8342
##     20       10.9077             nan     0.1000    0.4873
##     40        5.1754             nan     0.1000    0.1255
##     60        3.9780             nan     0.1000    0.0361
##     80        3.6496             nan     0.1000   -0.0010
##    100        3.5213             nan     0.1000   -0.0211
##    120        3.3883             nan     0.1000   -0.0142
##    140        3.3020             nan     0.1000   -0.0138
##    160        3.2203             nan     0.1000   -0.0216
##    180        3.1605             nan     0.1000   -0.0152
##    200        3.0923             nan     0.1000   -0.0198
##    220        3.0479             nan     0.1000   -0.0279
##    240        2.9893             nan     0.1000   -0.0241
##    260        2.9363             nan     0.1000   -0.0234
##    280        2.8916             nan     0.1000   -0.0058
##    300        2.8653             nan     0.1000   -0.0224
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.4362             nan     0.1000    7.0030
##      2       46.6116             nan     0.1000    5.6361
##      3       42.0642             nan     0.1000    4.5335
##      4       37.7826             nan     0.1000    4.1554
##      5       33.8582             nan     0.1000    3.5498
##      6       30.6153             nan     0.1000    3.1498
##      7       27.8829             nan     0.1000    2.6934
##      8       25.2753             nan     0.1000    2.4935
##      9       23.1285             nan     0.1000    1.8595
##     10       20.9749             nan     0.1000    1.8857
##     20       10.9571             nan     0.1000    0.6122
##     40        5.3502             nan     0.1000    0.0779
##     60        4.1763             nan     0.1000   -0.0137
##     80        3.8024             nan     0.1000    0.0007
##    100        3.6066             nan     0.1000   -0.0004
##    120        3.4560             nan     0.1000   -0.0168
##    140        3.3229             nan     0.1000   -0.0074
##    160        3.2409             nan     0.1000   -0.0217
##    180        3.1511             nan     0.1000   -0.0155
##    200        3.0869             nan     0.1000   -0.0089
##    220        3.0271             nan     0.1000   -0.0201
##    240        2.9514             nan     0.1000   -0.0077
##    260        2.8977             nan     0.1000   -0.0229
##    280        2.8568             nan     0.1000   -0.0198
##    300        2.8289             nan     0.1000   -0.0302
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.0270             nan     0.1000    6.8942
##      2       46.1065             nan     0.1000    5.8935
##      3       41.0751             nan     0.1000    4.6510
##      4       36.9179             nan     0.1000    3.5711
##      5       32.7213             nan     0.1000    4.1847
##      6       30.0405             nan     0.1000    2.7369
##      7       27.1135             nan     0.1000    3.0482
##      8       24.6044             nan     0.1000    2.2309
##      9       22.4807             nan     0.1000    1.8250
##     10       20.5759             nan     0.1000    1.5399
##     20       11.0405             nan     0.1000    0.6537
##     40        5.4706             nan     0.1000    0.0742
##     60        4.4475             nan     0.1000   -0.0146
##     80        4.1196             nan     0.1000   -0.0074
##    100        3.9276             nan     0.1000   -0.0098
##    120        3.7744             nan     0.1000   -0.0933
##    140        3.6593             nan     0.1000   -0.0394
##    160        3.5600             nan     0.1000   -0.0037
##    180        3.4577             nan     0.1000   -0.0154
##    200        3.3640             nan     0.1000   -0.0251
##    220        3.2994             nan     0.1000   -0.0099
##    240        3.2379             nan     0.1000   -0.0178
##    260        3.1911             nan     0.1000   -0.0169
##    280        3.1438             nan     0.1000   -0.0178
##    300        3.0936             nan     0.1000   -0.0188
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.3739             nan     0.1000    9.3698
##      2       42.4066             nan     0.1000    7.9101
##      3       35.9464             nan     0.1000    6.1595
##      4       30.7062             nan     0.1000    4.9506
##      5       26.4514             nan     0.1000    4.3995
##      6       22.7113             nan     0.1000    3.8440
##      7       19.7809             nan     0.1000    2.9080
##      8       17.1170             nan     0.1000    2.4644
##      9       15.0812             nan     0.1000    1.8732
##     10       13.5011             nan     0.1000    1.6270
##     20        5.6571             nan     0.1000    0.3023
##     40        3.2089             nan     0.1000    0.0003
##     60        2.7183             nan     0.1000   -0.1074
##     80        2.3969             nan     0.1000   -0.0318
##    100        2.1714             nan     0.1000   -0.0427
##    120        1.9679             nan     0.1000   -0.0297
##    140        1.7991             nan     0.1000   -0.0175
##    160        1.6459             nan     0.1000   -0.0251
##    180        1.5194             nan     0.1000   -0.0260
##    200        1.4013             nan     0.1000   -0.0079
##    220        1.3234             nan     0.1000   -0.0060
##    240        1.2112             nan     0.1000   -0.0197
##    260        1.1298             nan     0.1000   -0.0158
##    280        1.0584             nan     0.1000   -0.0079
##    300        0.9908             nan     0.1000   -0.0168
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.1423             nan     0.1000    9.6209
##      2       42.2315             nan     0.1000    7.6145
##      3       35.5985             nan     0.1000    6.9319
##      4       30.6417             nan     0.1000    4.9830
##      5       26.4719             nan     0.1000    4.1577
##      6       23.1671             nan     0.1000    3.0891
##      7       20.0749             nan     0.1000    2.8474
##      8       17.4301             nan     0.1000    2.4057
##      9       15.2617             nan     0.1000    2.2254
##     10       13.4821             nan     0.1000    1.4544
##     20        5.7217             nan     0.1000    0.2402
##     40        3.2569             nan     0.1000    0.0051
##     60        2.8185             nan     0.1000   -0.0257
##     80        2.4921             nan     0.1000   -0.0244
##    100        2.2173             nan     0.1000   -0.0101
##    120        2.0746             nan     0.1000   -0.0171
##    140        1.9363             nan     0.1000   -0.0171
##    160        1.8394             nan     0.1000   -0.0283
##    180        1.7516             nan     0.1000   -0.0347
##    200        1.6615             nan     0.1000   -0.0339
##    220        1.5769             nan     0.1000   -0.0080
##    240        1.4851             nan     0.1000   -0.0231
##    260        1.4077             nan     0.1000   -0.0137
##    280        1.3438             nan     0.1000   -0.0256
##    300        1.2647             nan     0.1000   -0.0199
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.4355             nan     0.1000    9.1791
##      2       42.7613             nan     0.1000    8.2567
##      3       36.2901             nan     0.1000    6.3323
##      4       30.8923             nan     0.1000    4.7599
##      5       26.5866             nan     0.1000    3.7903
##      6       22.9649             nan     0.1000    3.4182
##      7       19.7538             nan     0.1000    2.9222
##      8       17.4859             nan     0.1000    2.0773
##      9       15.3692             nan     0.1000    1.5229
##     10       13.7406             nan     0.1000    1.5391
##     20        5.9899             nan     0.1000    0.1866
##     40        3.6544             nan     0.1000   -0.0130
##     60        3.1821             nan     0.1000   -0.0339
##     80        2.9197             nan     0.1000   -0.0033
##    100        2.7338             nan     0.1000   -0.0081
##    120        2.5220             nan     0.1000   -0.0506
##    140        2.4259             nan     0.1000   -0.0428
##    160        2.3044             nan     0.1000   -0.0218
##    180        2.1652             nan     0.1000   -0.0086
##    200        2.0658             nan     0.1000   -0.0306
##    220        1.9594             nan     0.1000   -0.0150
##    240        1.8706             nan     0.1000   -0.0244
##    260        1.7823             nan     0.1000   -0.0135
##    280        1.7086             nan     0.1000   -0.0217
##    300        1.6343             nan     0.1000   -0.0141
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.0358             nan     0.1000    9.6039
##      2       41.9071             nan     0.1000    8.7358
##      3       35.3512             nan     0.1000    6.0948
##      4       29.8506             nan     0.1000    5.4915
##      5       24.9486             nan     0.1000    4.1676
##      6       21.3502             nan     0.1000    3.7699
##      7       18.3742             nan     0.1000    3.0507
##      8       15.7390             nan     0.1000    2.5529
##      9       13.8129             nan     0.1000    2.2162
##     10       12.1372             nan     0.1000    1.7408
##     20        4.4674             nan     0.1000    0.3276
##     40        2.5738             nan     0.1000   -0.0508
##     60        2.0565             nan     0.1000   -0.0107
##     80        1.7035             nan     0.1000   -0.0131
##    100        1.4354             nan     0.1000   -0.0270
##    120        1.2558             nan     0.1000   -0.0128
##    140        1.0873             nan     0.1000   -0.0285
##    160        0.9588             nan     0.1000   -0.0188
##    180        0.8409             nan     0.1000   -0.0144
##    200        0.7396             nan     0.1000   -0.0125
##    220        0.6429             nan     0.1000   -0.0117
##    240        0.5773             nan     0.1000   -0.0071
##    260        0.5147             nan     0.1000   -0.0098
##    280        0.4687             nan     0.1000   -0.0086
##    300        0.4289             nan     0.1000   -0.0059
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       49.3781             nan     0.1000   10.2082
##      2       41.2664             nan     0.1000    8.5324
##      3       34.9080             nan     0.1000    6.8117
##      4       29.1983             nan     0.1000    6.2143
##      5       25.0246             nan     0.1000    4.3415
##      6       21.2041             nan     0.1000    3.5683
##      7       18.2219             nan     0.1000    2.8348
##      8       15.5300             nan     0.1000    2.5217
##      9       13.6710             nan     0.1000    1.8149
##     10       12.0531             nan     0.1000    1.5602
##     20        4.6794             nan     0.1000    0.2904
##     40        2.8004             nan     0.1000    0.0110
##     60        2.3400             nan     0.1000   -0.0204
##     80        2.0768             nan     0.1000   -0.0229
##    100        1.8216             nan     0.1000   -0.0276
##    120        1.6084             nan     0.1000   -0.0209
##    140        1.4537             nan     0.1000   -0.0200
##    160        1.3244             nan     0.1000   -0.0285
##    180        1.2165             nan     0.1000   -0.0214
##    200        1.1014             nan     0.1000   -0.0251
##    220        0.9935             nan     0.1000   -0.0131
##    240        0.9135             nan     0.1000   -0.0087
##    260        0.8482             nan     0.1000   -0.0224
##    280        0.7740             nan     0.1000   -0.0110
##    300        0.7205             nan     0.1000   -0.0115
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       49.8131             nan     0.1000   10.3738
##      2       41.1052             nan     0.1000    8.3125
##      3       34.7142             nan     0.1000    6.4450
##      4       29.3390             nan     0.1000    5.3870
##      5       25.2650             nan     0.1000    4.5349
##      6       21.6314             nan     0.1000    3.5284
##      7       18.6077             nan     0.1000    2.8149
##      8       16.1288             nan     0.1000    2.3862
##      9       14.0012             nan     0.1000    1.8217
##     10       12.3978             nan     0.1000    1.6263
##     20        5.2632             nan     0.1000    0.2041
##     40        3.2783             nan     0.1000   -0.0134
##     60        2.7730             nan     0.1000   -0.0550
##     80        2.3891             nan     0.1000   -0.0286
##    100        2.1456             nan     0.1000   -0.0409
##    120        2.0076             nan     0.1000   -0.0448
##    140        1.8535             nan     0.1000   -0.0250
##    160        1.7237             nan     0.1000   -0.0103
##    180        1.6003             nan     0.1000   -0.0247
##    200        1.5004             nan     0.1000   -0.0153
##    220        1.3819             nan     0.1000   -0.0124
##    240        1.2903             nan     0.1000   -0.0156
##    260        1.2056             nan     0.1000   -0.0210
##    280        1.1289             nan     0.1000   -0.0183
##    300        1.0644             nan     0.1000   -0.0251
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.8622             nan     0.0100    0.7858
##      2       60.1215             nan     0.0100    0.7612
##      3       59.3351             nan     0.0100    0.7847
##      4       58.5539             nan     0.0100    0.7021
##      5       57.7923             nan     0.0100    0.6952
##      6       57.0916             nan     0.0100    0.7035
##      7       56.4124             nan     0.0100    0.6949
##      8       55.7605             nan     0.0100    0.6829
##      9       55.0238             nan     0.0100    0.6909
##     10       54.3772             nan     0.0100    0.6815
##     20       48.1879             nan     0.0100    0.5441
##     40       38.8915             nan     0.0100    0.4001
##     60       31.7702             nan     0.0100    0.2475
##     80       26.2876             nan     0.0100    0.2742
##    100       22.2078             nan     0.0100    0.1748
##    120       18.8923             nan     0.0100    0.1349
##    140       16.3259             nan     0.0100    0.0843
##    160       14.2958             nan     0.0100    0.0954
##    180       12.6315             nan     0.0100    0.0591
##    200       11.2910             nan     0.0100    0.0561
##    220       10.1998             nan     0.0100    0.0392
##    240        9.2695             nan     0.0100    0.0391
##    260        8.4474             nan     0.0100    0.0347
##    280        7.7501             nan     0.0100    0.0161
##    300        7.1513             nan     0.0100    0.0146
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.8854             nan     0.0100    0.6969
##      2       60.1073             nan     0.0100    0.7310
##      3       59.3967             nan     0.0100    0.7999
##      4       58.6698             nan     0.0100    0.6938
##      5       57.9319             nan     0.0100    0.7599
##      6       57.2155             nan     0.0100    0.7338
##      7       56.4624             nan     0.0100    0.6651
##      8       55.7420             nan     0.0100    0.6485
##      9       55.0418             nan     0.0100    0.6859
##     10       54.3651             nan     0.0100    0.6285
##     20       48.4772             nan     0.0100    0.4640
##     40       39.0947             nan     0.0100    0.3918
##     60       32.0169             nan     0.0100    0.3185
##     80       26.5058             nan     0.0100    0.2158
##    100       22.3451             nan     0.0100    0.1622
##    120       19.1111             nan     0.0100    0.1380
##    140       16.4672             nan     0.0100    0.1122
##    160       14.4559             nan     0.0100    0.0706
##    180       12.7613             nan     0.0100    0.0576
##    200       11.4189             nan     0.0100    0.0582
##    220       10.2943             nan     0.0100    0.0521
##    240        9.3462             nan     0.0100    0.0483
##    260        8.5165             nan     0.0100    0.0336
##    280        7.8204             nan     0.0100    0.0320
##    300        7.2084             nan     0.0100    0.0127
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.8515             nan     0.0100    0.7710
##      2       60.0740             nan     0.0100    0.7560
##      3       59.3386             nan     0.0100    0.7130
##      4       58.5591             nan     0.0100    0.7299
##      5       57.7918             nan     0.0100    0.7060
##      6       57.0935             nan     0.0100    0.6704
##      7       56.3776             nan     0.0100    0.7286
##      8       55.6486             nan     0.0100    0.7283
##      9       54.9781             nan     0.0100    0.6806
##     10       54.3195             nan     0.0100    0.6347
##     20       48.4234             nan     0.0100    0.5273
##     40       38.8108             nan     0.0100    0.4193
##     60       31.8217             nan     0.0100    0.2951
##     80       26.3999             nan     0.0100    0.2231
##    100       22.2229             nan     0.0100    0.1660
##    120       18.9869             nan     0.0100    0.1055
##    140       16.5039             nan     0.0100    0.0999
##    160       14.4590             nan     0.0100    0.0799
##    180       12.8316             nan     0.0100    0.0526
##    200       11.5245             nan     0.0100    0.0490
##    220       10.3755             nan     0.0100    0.0390
##    240        9.4417             nan     0.0100    0.0358
##    260        8.6588             nan     0.0100    0.0264
##    280        8.0043             nan     0.0100    0.0232
##    300        7.4034             nan     0.0100    0.0221
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.6244             nan     0.0100    1.0422
##      2       59.5691             nan     0.0100    1.0235
##      3       58.5444             nan     0.0100    0.9633
##      4       57.5907             nan     0.0100    0.9335
##      5       56.6268             nan     0.0100    0.9290
##      6       55.6790             nan     0.0100    0.8467
##      7       54.7621             nan     0.0100    0.9628
##      8       53.8603             nan     0.0100    0.8882
##      9       53.0019             nan     0.0100    0.7640
##     10       52.1268             nan     0.0100    0.8619
##     20       44.2308             nan     0.0100    0.6923
##     40       32.6187             nan     0.0100    0.5073
##     60       24.4035             nan     0.0100    0.3343
##     80       18.6945             nan     0.0100    0.2406
##    100       14.5752             nan     0.0100    0.1588
##    120       11.6856             nan     0.0100    0.1103
##    140        9.5337             nan     0.0100    0.0979
##    160        7.8776             nan     0.0100    0.0680
##    180        6.6497             nan     0.0100    0.0488
##    200        5.7382             nan     0.0100    0.0406
##    220        5.0562             nan     0.0100    0.0282
##    240        4.5313             nan     0.0100    0.0166
##    260        4.1174             nan     0.0100    0.0002
##    280        3.7969             nan     0.0100    0.0108
##    300        3.5617             nan     0.0100    0.0087
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.6497             nan     0.0100    1.0361
##      2       59.6352             nan     0.0100    1.0197
##      3       58.6378             nan     0.0100    1.0019
##      4       57.6418             nan     0.0100    0.9638
##      5       56.6943             nan     0.0100    0.9908
##      6       55.7101             nan     0.0100    0.9005
##      7       54.7836             nan     0.0100    0.8536
##      8       53.9006             nan     0.0100    0.8389
##      9       53.0299             nan     0.0100    0.8221
##     10       52.1675             nan     0.0100    0.8277
##     20       44.4035             nan     0.0100    0.7242
##     40       32.6232             nan     0.0100    0.4740
##     60       24.3441             nan     0.0100    0.3631
##     80       18.5692             nan     0.0100    0.2625
##    100       14.5074             nan     0.0100    0.1387
##    120       11.5852             nan     0.0100    0.1115
##    140        9.4717             nan     0.0100    0.0752
##    160        7.8864             nan     0.0100    0.0677
##    180        6.6630             nan     0.0100    0.0281
##    200        5.7928             nan     0.0100    0.0306
##    220        5.1096             nan     0.0100    0.0206
##    240        4.6103             nan     0.0100    0.0175
##    260        4.2160             nan     0.0100    0.0154
##    280        3.9180             nan     0.0100    0.0018
##    300        3.6937             nan     0.0100    0.0024
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.6556             nan     0.0100    1.1317
##      2       59.6703             nan     0.0100    0.9472
##      3       58.7070             nan     0.0100    0.9769
##      4       57.7343             nan     0.0100    0.8783
##      5       56.7474             nan     0.0100    0.9332
##      6       55.8237             nan     0.0100    0.8892
##      7       54.9019             nan     0.0100    0.9539
##      8       54.0377             nan     0.0100    0.7506
##      9       53.1751             nan     0.0100    0.9145
##     10       52.3000             nan     0.0100    0.9598
##     20       44.4790             nan     0.0100    0.7267
##     40       32.7309             nan     0.0100    0.4784
##     60       24.4643             nan     0.0100    0.3220
##     80       18.7168             nan     0.0100    0.2286
##    100       14.7281             nan     0.0100    0.1624
##    120       11.8583             nan     0.0100    0.1210
##    140        9.7632             nan     0.0100    0.0894
##    160        8.1877             nan     0.0100    0.0588
##    180        7.0150             nan     0.0100    0.0585
##    200        6.0665             nan     0.0100    0.0416
##    220        5.3694             nan     0.0100    0.0266
##    240        4.8548             nan     0.0100    0.0183
##    260        4.4771             nan     0.0100    0.0124
##    280        4.1755             nan     0.0100    0.0083
##    300        3.9376             nan     0.0100    0.0031
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.5869             nan     0.0100    1.1631
##      2       59.5195             nan     0.0100    0.9420
##      3       58.4932             nan     0.0100    0.9698
##      4       57.4808             nan     0.0100    0.8713
##      5       56.4547             nan     0.0100    1.0556
##      6       55.4551             nan     0.0100    0.8990
##      7       54.4914             nan     0.0100    0.8823
##      8       53.5410             nan     0.0100    0.9121
##      9       52.5978             nan     0.0100    0.8866
##     10       51.7141             nan     0.0100    0.8587
##     20       43.5499             nan     0.0100    0.7624
##     40       31.1845             nan     0.0100    0.5068
##     60       22.7847             nan     0.0100    0.3511
##     80       16.9593             nan     0.0100    0.2376
##    100       12.8032             nan     0.0100    0.1515
##    120        9.8701             nan     0.0100    0.1090
##    140        7.8855             nan     0.0100    0.0831
##    160        6.3999             nan     0.0100    0.0507
##    180        5.3660             nan     0.0100    0.0320
##    200        4.5623             nan     0.0100    0.0250
##    220        3.9722             nan     0.0100    0.0157
##    240        3.5423             nan     0.0100    0.0107
##    260        3.2097             nan     0.0100    0.0054
##    280        2.9834             nan     0.0100    0.0038
##    300        2.7856             nan     0.0100    0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.5776             nan     0.0100    1.0296
##      2       59.4933             nan     0.0100    1.0543
##      3       58.4766             nan     0.0100    1.0763
##      4       57.4287             nan     0.0100    1.0608
##      5       56.4451             nan     0.0100    0.9021
##      6       55.4892             nan     0.0100    0.9716
##      7       54.5444             nan     0.0100    1.0167
##      8       53.6095             nan     0.0100    0.9541
##      9       52.6849             nan     0.0100    0.9316
##     10       51.7966             nan     0.0100    0.9580
##     20       43.5624             nan     0.0100    0.7522
##     40       31.1349             nan     0.0100    0.5174
##     60       22.7035             nan     0.0100    0.3389
##     80       16.8254             nan     0.0100    0.2484
##    100       12.8450             nan     0.0100    0.1585
##    120        9.9603             nan     0.0100    0.1072
##    140        7.9663             nan     0.0100    0.0834
##    160        6.5299             nan     0.0100    0.0553
##    180        5.4903             nan     0.0100    0.0396
##    200        4.7390             nan     0.0100    0.0211
##    220        4.1786             nan     0.0100    0.0247
##    240        3.7715             nan     0.0100    0.0042
##    260        3.4738             nan     0.0100    0.0031
##    280        3.2369             nan     0.0100    0.0088
##    300        3.0487             nan     0.0100   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.5996             nan     0.0100    0.8953
##      2       59.5606             nan     0.0100    1.1262
##      3       58.5221             nan     0.0100    0.9993
##      4       57.5063             nan     0.0100    1.0563
##      5       56.5369             nan     0.0100    0.9514
##      6       55.5624             nan     0.0100    1.0539
##      7       54.6471             nan     0.0100    0.9706
##      8       53.7201             nan     0.0100    0.8989
##      9       52.8280             nan     0.0100    0.9081
##     10       51.9032             nan     0.0100    0.8896
##     20       43.6068             nan     0.0100    0.7088
##     40       31.2918             nan     0.0100    0.5292
##     60       22.8699             nan     0.0100    0.3431
##     80       17.1338             nan     0.0100    0.2524
##    100       13.1551             nan     0.0100    0.1440
##    120       10.3012             nan     0.0100    0.1000
##    140        8.3062             nan     0.0100    0.0685
##    160        6.8389             nan     0.0100    0.0501
##    180        5.7844             nan     0.0100    0.0352
##    200        5.0482             nan     0.0100    0.0243
##    220        4.5270             nan     0.0100    0.0179
##    240        4.1464             nan     0.0100    0.0141
##    260        3.8453             nan     0.0100    0.0092
##    280        3.6180             nan     0.0100    0.0048
##    300        3.4441             nan     0.0100    0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.8659             nan     0.0500    3.8502
##      2       54.3191             nan     0.0500    2.9817
##      3       51.1178             nan     0.0500    2.9764
##      4       48.5175             nan     0.0500    2.6572
##      5       45.8940             nan     0.0500    2.7339
##      6       43.4309             nan     0.0500    2.4136
##      7       41.1371             nan     0.0500    2.0524
##      8       38.9164             nan     0.0500    1.9734
##      9       36.7036             nan     0.0500    1.9358
##     10       34.8032             nan     0.0500    1.6942
##     20       22.1542             nan     0.0500    0.8881
##     40       11.3766             nan     0.0500    0.2618
##     60        7.3514             nan     0.0500    0.1510
##     80        5.4349             nan     0.0500    0.0575
##    100        4.4834             nan     0.0500    0.0130
##    120        4.0175             nan     0.0500   -0.0022
##    140        3.7737             nan     0.0500   -0.0117
##    160        3.6121             nan     0.0500   -0.0094
##    180        3.4774             nan     0.0500   -0.0161
##    200        3.3913             nan     0.0500   -0.0053
##    220        3.3333             nan     0.0500   -0.0225
##    240        3.2593             nan     0.0500   -0.0095
##    260        3.2002             nan     0.0500   -0.0098
##    280        3.1515             nan     0.0500   -0.0057
##    300        3.1130             nan     0.0500   -0.0095
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.7882             nan     0.0500    3.9965
##      2       54.1363             nan     0.0500    3.3503
##      3       51.1976             nan     0.0500    2.8074
##      4       48.0151             nan     0.0500    2.9378
##      5       45.1334             nan     0.0500    2.6864
##      6       42.6746             nan     0.0500    2.1181
##      7       40.2941             nan     0.0500    2.0977
##      8       38.1431             nan     0.0500    1.9014
##      9       36.2302             nan     0.0500    1.8723
##     10       34.5503             nan     0.0500    1.5922
##     20       21.2147             nan     0.0500    0.8317
##     40       11.1262             nan     0.0500    0.2396
##     60        7.2280             nan     0.0500    0.1520
##     80        5.3339             nan     0.0500    0.0400
##    100        4.4383             nan     0.0500    0.0149
##    120        3.9916             nan     0.0500    0.0022
##    140        3.7708             nan     0.0500   -0.0056
##    160        3.6301             nan     0.0500   -0.0070
##    180        3.5340             nan     0.0500   -0.0038
##    200        3.4543             nan     0.0500   -0.0169
##    220        3.3869             nan     0.0500   -0.0061
##    240        3.3166             nan     0.0500   -0.0064
##    260        3.2699             nan     0.0500   -0.0217
##    280        3.2148             nan     0.0500   -0.0051
##    300        3.1730             nan     0.0500   -0.0054
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.1797             nan     0.0500    3.7733
##      2       54.3168             nan     0.0500    3.4353
##      3       51.3493             nan     0.0500    2.9450
##      4       48.3747             nan     0.0500    2.5359
##      5       45.7498             nan     0.0500    2.9776
##      6       43.3822             nan     0.0500    2.4471
##      7       40.9736             nan     0.0500    2.1355
##      8       38.6822             nan     0.0500    1.9300
##      9       36.8104             nan     0.0500    1.6827
##     10       34.7779             nan     0.0500    1.9006
##     20       22.1031             nan     0.0500    0.8688
##     40       11.2554             nan     0.0500    0.2471
##     60        7.3428             nan     0.0500    0.0969
##     80        5.5532             nan     0.0500    0.0223
##    100        4.7214             nan     0.0500    0.0034
##    120        4.3264             nan     0.0500    0.0089
##    140        4.0786             nan     0.0500    0.0054
##    160        3.9044             nan     0.0500   -0.0118
##    180        3.7743             nan     0.0500   -0.0014
##    200        3.6640             nan     0.0500   -0.0067
##    220        3.5821             nan     0.0500   -0.0008
##    240        3.5075             nan     0.0500   -0.0135
##    260        3.4452             nan     0.0500   -0.0052
##    280        3.3742             nan     0.0500   -0.0028
##    300        3.3140             nan     0.0500   -0.0089
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.6147             nan     0.0500    4.6856
##      2       51.9532             nan     0.0500    4.5843
##      3       47.8935             nan     0.0500    4.3724
##      4       44.1853             nan     0.0500    3.8858
##      5       40.7138             nan     0.0500    3.3027
##      6       37.6232             nan     0.0500    2.9641
##      7       34.9742             nan     0.0500    2.8070
##      8       32.3039             nan     0.0500    2.0199
##      9       30.1140             nan     0.0500    2.4929
##     10       27.9446             nan     0.0500    2.1338
##     20       14.7119             nan     0.0500    0.9035
##     40        5.7641             nan     0.0500    0.1531
##     60        3.5447             nan     0.0500    0.0286
##     80        2.8705             nan     0.0500   -0.0125
##    100        2.5672             nan     0.0500   -0.0001
##    120        2.3372             nan     0.0500   -0.0214
##    140        2.1622             nan     0.0500   -0.0159
##    160        2.0329             nan     0.0500   -0.0108
##    180        1.8908             nan     0.0500   -0.0082
##    200        1.7877             nan     0.0500   -0.0048
##    220        1.7013             nan     0.0500   -0.0177
##    240        1.6098             nan     0.0500   -0.0051
##    260        1.5338             nan     0.0500   -0.0086
##    280        1.4620             nan     0.0500   -0.0124
##    300        1.3867             nan     0.0500   -0.0073
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.7898             nan     0.0500    4.8951
##      2       52.0735             nan     0.0500    4.4419
##      3       48.0155             nan     0.0500    3.9285
##      4       44.4241             nan     0.0500    4.0645
##      5       40.9792             nan     0.0500    3.2920
##      6       37.9587             nan     0.0500    2.7786
##      7       35.1453             nan     0.0500    2.7625
##      8       32.5770             nan     0.0500    2.7282
##      9       30.1189             nan     0.0500    2.2010
##     10       27.9742             nan     0.0500    2.3167
##     20       14.4494             nan     0.0500    0.9264
##     40        5.7708             nan     0.0500    0.1227
##     60        3.7063             nan     0.0500    0.0164
##     80        3.1016             nan     0.0500   -0.0041
##    100        2.7879             nan     0.0500   -0.0016
##    120        2.5743             nan     0.0500   -0.0170
##    140        2.4015             nan     0.0500   -0.0064
##    160        2.2825             nan     0.0500   -0.0010
##    180        2.1594             nan     0.0500   -0.0077
##    200        2.0689             nan     0.0500   -0.0166
##    220        1.9922             nan     0.0500   -0.0074
##    240        1.9112             nan     0.0500   -0.0172
##    260        1.8217             nan     0.0500   -0.0100
##    280        1.7426             nan     0.0500   -0.0062
##    300        1.6689             nan     0.0500   -0.0057
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.8525             nan     0.0500    5.0353
##      2       52.2781             nan     0.0500    4.6157
##      3       48.2328             nan     0.0500    3.8441
##      4       44.6826             nan     0.0500    3.9135
##      5       41.1594             nan     0.0500    3.5338
##      6       38.2077             nan     0.0500    3.0143
##      7       35.3578             nan     0.0500    2.9199
##      8       32.7042             nan     0.0500    2.4874
##      9       30.3569             nan     0.0500    2.2613
##     10       28.2970             nan     0.0500    1.8581
##     20       14.5350             nan     0.0500    0.6874
##     40        6.1512             nan     0.0500    0.1254
##     60        4.0354             nan     0.0500    0.0327
##     80        3.4133             nan     0.0500    0.0098
##    100        3.1163             nan     0.0500   -0.0093
##    120        2.9128             nan     0.0500   -0.0379
##    140        2.7340             nan     0.0500   -0.0071
##    160        2.6080             nan     0.0500   -0.0191
##    180        2.5071             nan     0.0500   -0.0066
##    200        2.4102             nan     0.0500   -0.0176
##    220        2.3238             nan     0.0500   -0.0201
##    240        2.2269             nan     0.0500   -0.0106
##    260        2.1276             nan     0.0500   -0.0070
##    280        2.0660             nan     0.0500   -0.0171
##    300        1.9850             nan     0.0500   -0.0064
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.4497             nan     0.0500    5.2571
##      2       51.4948             nan     0.0500    4.3872
##      3       47.2237             nan     0.0500    4.2788
##      4       43.1012             nan     0.0500    3.9568
##      5       39.6110             nan     0.0500    3.7716
##      6       36.4250             nan     0.0500    3.1385
##      7       33.5711             nan     0.0500    2.8407
##      8       31.0495             nan     0.0500    2.4829
##      9       28.6009             nan     0.0500    2.5751
##     10       26.3251             nan     0.0500    2.1336
##     20       12.4240             nan     0.0500    0.8579
##     40        4.4064             nan     0.0500    0.0845
##     60        2.8496             nan     0.0500    0.0214
##     80        2.3113             nan     0.0500   -0.0207
##    100        1.9595             nan     0.0500    0.0034
##    120        1.7357             nan     0.0500   -0.0081
##    140        1.5718             nan     0.0500   -0.0148
##    160        1.4374             nan     0.0500   -0.0039
##    180        1.3285             nan     0.0500   -0.0044
##    200        1.2149             nan     0.0500   -0.0072
##    220        1.1142             nan     0.0500   -0.0084
##    240        1.0212             nan     0.0500   -0.0084
##    260        0.9448             nan     0.0500   -0.0079
##    280        0.8773             nan     0.0500   -0.0051
##    300        0.8178             nan     0.0500   -0.0071
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.1252             nan     0.0500    5.7414
##      2       51.4204             nan     0.0500    4.1735
##      3       46.8492             nan     0.0500    4.3520
##      4       42.9720             nan     0.0500    3.6947
##      5       39.4560             nan     0.0500    3.4321
##      6       36.0655             nan     0.0500    3.3346
##      7       33.0533             nan     0.0500    2.4446
##      8       30.4136             nan     0.0500    2.4410
##      9       27.9294             nan     0.0500    2.0733
##     10       25.8751             nan     0.0500    1.9461
##     20       12.3666             nan     0.0500    0.7241
##     40        4.5891             nan     0.0500    0.1474
##     60        3.0616             nan     0.0500    0.0053
##     80        2.5462             nan     0.0500   -0.0135
##    100        2.2825             nan     0.0500    0.0018
##    120        2.0888             nan     0.0500   -0.0110
##    140        1.9180             nan     0.0500   -0.0089
##    160        1.7699             nan     0.0500   -0.0045
##    180        1.6561             nan     0.0500   -0.0124
##    200        1.5505             nan     0.0500   -0.0150
##    220        1.4600             nan     0.0500   -0.0164
##    240        1.3829             nan     0.0500   -0.0084
##    260        1.3043             nan     0.0500   -0.0112
##    280        1.2221             nan     0.0500   -0.0104
##    300        1.1498             nan     0.0500   -0.0104
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.5323             nan     0.0500    4.4028
##      2       51.6696             nan     0.0500    4.9683
##      3       47.4301             nan     0.0500    4.4332
##      4       43.4831             nan     0.0500    3.5643
##      5       39.9808             nan     0.0500    3.6605
##      6       36.7691             nan     0.0500    2.8788
##      7       33.8249             nan     0.0500    2.6688
##      8       31.2318             nan     0.0500    2.5521
##      9       28.7939             nan     0.0500    2.2137
##     10       26.6234             nan     0.0500    2.2237
##     20       12.8953             nan     0.0500    0.9425
##     40        5.0654             nan     0.0500    0.1479
##     60        3.4858             nan     0.0500   -0.0025
##     80        2.9627             nan     0.0500   -0.0176
##    100        2.6740             nan     0.0500   -0.0081
##    120        2.4513             nan     0.0500   -0.0176
##    140        2.2664             nan     0.0500   -0.0130
##    160        2.1224             nan     0.0500   -0.0156
##    180        1.9930             nan     0.0500   -0.0065
##    200        1.8956             nan     0.0500   -0.0073
##    220        1.8014             nan     0.0500   -0.0104
##    240        1.7221             nan     0.0500   -0.0064
##    260        1.6321             nan     0.0500   -0.0195
##    280        1.5648             nan     0.0500   -0.0026
##    300        1.5015             nan     0.0500   -0.0089
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.9441             nan     0.1000    7.2420
##      2       47.8271             nan     0.1000    6.1404
##      3       42.9778             nan     0.1000    5.0376
##      4       38.4820             nan     0.1000    4.3274
##      5       34.5962             nan     0.1000    3.3048
##      6       31.2972             nan     0.1000    3.4166
##      7       28.1830             nan     0.1000    2.7391
##      8       26.0307             nan     0.1000    2.2424
##      9       23.8398             nan     0.1000    1.9182
##     10       21.7298             nan     0.1000    1.9186
##     20       10.8547             nan     0.1000    0.4527
##     40        5.1427             nan     0.1000    0.0812
##     60        3.8430             nan     0.1000   -0.0041
##     80        3.4858             nan     0.1000   -0.0395
##    100        3.2962             nan     0.1000   -0.0252
##    120        3.1812             nan     0.1000   -0.0162
##    140        3.1310             nan     0.1000   -0.0203
##    160        3.0434             nan     0.1000   -0.0263
##    180        2.9638             nan     0.1000   -0.0199
##    200        2.8911             nan     0.1000   -0.0234
##    220        2.8315             nan     0.1000   -0.0251
##    240        2.7713             nan     0.1000   -0.0103
##    260        2.7231             nan     0.1000   -0.0212
##    280        2.6790             nan     0.1000   -0.0173
##    300        2.6449             nan     0.1000   -0.0278
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.2811             nan     0.1000    7.7156
##      2       48.5306             nan     0.1000    5.1232
##      3       43.1711             nan     0.1000    5.3144
##      4       38.7685             nan     0.1000    4.2570
##      5       35.3232             nan     0.1000    3.6249
##      6       31.6729             nan     0.1000    3.3728
##      7       28.6366             nan     0.1000    2.9479
##      8       26.3530             nan     0.1000    2.1285
##      9       24.2800             nan     0.1000    2.1439
##     10       22.1598             nan     0.1000    1.8364
##     20       11.3253             nan     0.1000    0.7188
##     40        5.3606             nan     0.1000    0.0523
##     60        4.0782             nan     0.1000    0.0203
##     80        3.7381             nan     0.1000   -0.0153
##    100        3.5337             nan     0.1000    0.0049
##    120        3.3590             nan     0.1000   -0.0180
##    140        3.2575             nan     0.1000   -0.0073
##    160        3.1802             nan     0.1000   -0.0311
##    180        3.0985             nan     0.1000   -0.0099
##    200        3.0269             nan     0.1000   -0.0199
##    220        2.9687             nan     0.1000   -0.0187
##    240        2.9297             nan     0.1000   -0.0084
##    260        2.8581             nan     0.1000   -0.0240
##    280        2.8179             nan     0.1000   -0.0120
##    300        2.7729             nan     0.1000   -0.0103
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.4760             nan     0.1000    7.6332
##      2       47.5863             nan     0.1000    5.2700
##      3       42.2104             nan     0.1000    5.3414
##      4       37.7294             nan     0.1000    4.1433
##      5       33.6294             nan     0.1000    3.6448
##      6       30.1534             nan     0.1000    3.0533
##      7       27.2197             nan     0.1000    2.7926
##      8       24.8262             nan     0.1000    1.9462
##      9       22.6172             nan     0.1000    2.1359
##     10       20.7690             nan     0.1000    1.9686
##     20       10.7421             nan     0.1000    0.4988
##     40        5.2405             nan     0.1000    0.0935
##     60        4.0977             nan     0.1000   -0.0076
##     80        3.7719             nan     0.1000    0.0003
##    100        3.5611             nan     0.1000   -0.0232
##    120        3.4025             nan     0.1000   -0.0198
##    140        3.2895             nan     0.1000   -0.0189
##    160        3.2133             nan     0.1000   -0.0145
##    180        3.1242             nan     0.1000   -0.0129
##    200        3.0728             nan     0.1000   -0.0185
##    220        2.9869             nan     0.1000   -0.0152
##    240        2.9350             nan     0.1000   -0.0125
##    260        2.8926             nan     0.1000   -0.0142
##    280        2.8366             nan     0.1000   -0.0054
##    300        2.8009             nan     0.1000   -0.0111
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.2862             nan     0.1000   10.3712
##      2       43.4136             nan     0.1000    6.2938
##      3       36.8516             nan     0.1000    6.5241
##      4       31.3711             nan     0.1000    5.7309
##      5       27.3633             nan     0.1000    4.0482
##      6       23.4733             nan     0.1000    3.8651
##      7       20.2912             nan     0.1000    3.2508
##      8       17.7051             nan     0.1000    2.4428
##      9       15.4944             nan     0.1000    1.9867
##     10       13.5572             nan     0.1000    1.8592
##     20        5.5870             nan     0.1000    0.3227
##     40        2.9582             nan     0.1000   -0.0131
##     60        2.4322             nan     0.1000    0.0033
##     80        2.1164             nan     0.1000   -0.0086
##    100        1.8792             nan     0.1000   -0.0453
##    120        1.7086             nan     0.1000   -0.0026
##    140        1.5568             nan     0.1000   -0.0170
##    160        1.4502             nan     0.1000   -0.0178
##    180        1.3317             nan     0.1000   -0.0101
##    200        1.2255             nan     0.1000   -0.0256
##    220        1.1453             nan     0.1000   -0.0156
##    240        1.0606             nan     0.1000   -0.0133
##    260        0.9894             nan     0.1000   -0.0109
##    280        0.9172             nan     0.1000   -0.0093
##    300        0.8677             nan     0.1000   -0.0068
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.7152             nan     0.1000    9.1557
##      2       43.8506             nan     0.1000    6.9289
##      3       37.0778             nan     0.1000    5.8324
##      4       31.7620             nan     0.1000    5.4133
##      5       27.2661             nan     0.1000    3.8028
##      6       23.4308             nan     0.1000    3.3356
##      7       20.2442             nan     0.1000    2.9293
##      8       17.7938             nan     0.1000    2.2916
##      9       15.8277             nan     0.1000    2.0609
##     10       13.9468             nan     0.1000    1.6470
##     20        5.6296             nan     0.1000    0.3829
##     40        3.1728             nan     0.1000   -0.0289
##     60        2.6891             nan     0.1000   -0.0633
##     80        2.3955             nan     0.1000   -0.0499
##    100        2.2032             nan     0.1000   -0.0224
##    120        2.0237             nan     0.1000   -0.0349
##    140        1.8670             nan     0.1000   -0.0168
##    160        1.7399             nan     0.1000   -0.0255
##    180        1.6253             nan     0.1000   -0.0057
##    200        1.4989             nan     0.1000   -0.0107
##    220        1.3953             nan     0.1000   -0.0080
##    240        1.3051             nan     0.1000   -0.0089
##    260        1.2243             nan     0.1000   -0.0156
##    280        1.1630             nan     0.1000   -0.0087
##    300        1.1043             nan     0.1000   -0.0125
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.4981             nan     0.1000    8.8722
##      2       43.6963             nan     0.1000    6.9849
##      3       37.1869             nan     0.1000    6.6393
##      4       31.9387             nan     0.1000    5.5176
##      5       27.3425             nan     0.1000    4.1106
##      6       23.4002             nan     0.1000    3.9126
##      7       20.4652             nan     0.1000    2.9522
##      8       17.9750             nan     0.1000    2.4698
##      9       15.8197             nan     0.1000    1.8620
##     10       13.8155             nan     0.1000    1.6188
##     20        5.8215             nan     0.1000    0.3007
##     40        3.4309             nan     0.1000   -0.0030
##     60        2.9204             nan     0.1000    0.0017
##     80        2.5932             nan     0.1000   -0.0239
##    100        2.3570             nan     0.1000   -0.0199
##    120        2.1887             nan     0.1000   -0.0062
##    140        2.0648             nan     0.1000   -0.0162
##    160        1.9457             nan     0.1000   -0.0180
##    180        1.8223             nan     0.1000   -0.0215
##    200        1.7148             nan     0.1000   -0.0220
##    220        1.6322             nan     0.1000   -0.0170
##    240        1.5625             nan     0.1000   -0.0116
##    260        1.5047             nan     0.1000   -0.0212
##    280        1.4416             nan     0.1000   -0.0127
##    300        1.3683             nan     0.1000   -0.0134
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.1744             nan     0.1000    9.3356
##      2       42.8662             nan     0.1000    7.7694
##      3       35.9292             nan     0.1000    6.2969
##      4       30.1164             nan     0.1000    5.0818
##      5       25.7409             nan     0.1000    4.2125
##      6       21.7417             nan     0.1000    3.8264
##      7       18.6213             nan     0.1000    3.0381
##      8       15.6919             nan     0.1000    2.4127
##      9       13.3818             nan     0.1000    2.0423
##     10       11.6152             nan     0.1000    1.2527
##     20        4.3874             nan     0.1000    0.2272
##     40        2.3717             nan     0.1000    0.0035
##     60        1.8397             nan     0.1000    0.0016
##     80        1.5327             nan     0.1000   -0.0209
##    100        1.3159             nan     0.1000   -0.0375
##    120        1.1107             nan     0.1000   -0.0204
##    140        0.9601             nan     0.1000   -0.0145
##    160        0.8381             nan     0.1000   -0.0111
##    180        0.7415             nan     0.1000   -0.0050
##    200        0.6485             nan     0.1000   -0.0091
##    220        0.5703             nan     0.1000   -0.0078
##    240        0.5192             nan     0.1000   -0.0112
##    260        0.4702             nan     0.1000   -0.0083
##    280        0.4251             nan     0.1000   -0.0114
##    300        0.3761             nan     0.1000   -0.0072
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.4627             nan     0.1000   10.8696
##      2       43.1587             nan     0.1000    8.7122
##      3       35.9611             nan     0.1000    6.8755
##      4       30.4888             nan     0.1000    4.8808
##      5       25.9710             nan     0.1000    4.9787
##      6       22.2309             nan     0.1000    3.3146
##      7       19.1096             nan     0.1000    3.0082
##      8       16.5596             nan     0.1000    2.3615
##      9       14.2646             nan     0.1000    1.8845
##     10       12.4170             nan     0.1000    1.8401
##     20        4.5808             nan     0.1000    0.2301
##     40        2.6547             nan     0.1000   -0.0467
##     60        2.2096             nan     0.1000   -0.0087
##     80        1.8899             nan     0.1000   -0.0253
##    100        1.6260             nan     0.1000   -0.0434
##    120        1.4313             nan     0.1000   -0.0183
##    140        1.2741             nan     0.1000   -0.0183
##    160        1.1372             nan     0.1000   -0.0096
##    180        1.0120             nan     0.1000   -0.0131
##    200        0.9245             nan     0.1000   -0.0136
##    220        0.8249             nan     0.1000   -0.0091
##    240        0.7598             nan     0.1000   -0.0113
##    260        0.6955             nan     0.1000   -0.0096
##    280        0.6406             nan     0.1000   -0.0045
##    300        0.5900             nan     0.1000   -0.0158
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.5008             nan     0.1000   10.6072
##      2       43.2465             nan     0.1000    8.1726
##      3       35.9253             nan     0.1000    7.0885
##      4       30.6692             nan     0.1000    5.1548
##      5       25.9915             nan     0.1000    4.4196
##      6       22.3171             nan     0.1000    3.6442
##      7       19.2065             nan     0.1000    2.9365
##      8       16.4438             nan     0.1000    2.4022
##      9       14.6115             nan     0.1000    2.1859
##     10       12.7389             nan     0.1000    1.6453
##     20        5.0581             nan     0.1000    0.2565
##     40        3.0549             nan     0.1000   -0.0185
##     60        2.5366             nan     0.1000   -0.0198
##     80        2.1868             nan     0.1000   -0.0116
##    100        1.9244             nan     0.1000   -0.0173
##    120        1.7636             nan     0.1000   -0.0295
##    140        1.6170             nan     0.1000   -0.0245
##    160        1.4767             nan     0.1000   -0.0115
##    180        1.3540             nan     0.1000   -0.0082
##    200        1.2397             nan     0.1000   -0.0068
##    220        1.1560             nan     0.1000   -0.0339
##    240        1.0796             nan     0.1000   -0.0165
##    260        1.0065             nan     0.1000   -0.0076
##    280        0.9416             nan     0.1000   -0.0055
##    300        0.8828             nan     0.1000   -0.0092
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.2879             nan     0.0100    0.7918
##      2       61.5685             nan     0.0100    0.7657
##      3       60.7858             nan     0.0100    0.7480
##      4       60.0537             nan     0.0100    0.6681
##      5       59.2752             nan     0.0100    0.7176
##      6       58.5217             nan     0.0100    0.7559
##      7       57.7783             nan     0.0100    0.6767
##      8       57.0283             nan     0.0100    0.7019
##      9       56.3555             nan     0.0100    0.6650
##     10       55.6776             nan     0.0100    0.6584
##     20       49.7642             nan     0.0100    0.5350
##     40       40.1004             nan     0.0100    0.3794
##     60       32.7544             nan     0.0100    0.2946
##     80       27.3242             nan     0.0100    0.2446
##    100       23.0147             nan     0.0100    0.1844
##    120       19.5939             nan     0.0100    0.1319
##    140       16.9658             nan     0.0100    0.1027
##    160       14.8136             nan     0.0100    0.0811
##    180       13.1525             nan     0.0100    0.0689
##    200       11.6936             nan     0.0100    0.0564
##    220       10.5508             nan     0.0100    0.0321
##    240        9.5690             nan     0.0100    0.0397
##    260        8.7437             nan     0.0100    0.0307
##    280        8.0567             nan     0.0100    0.0194
##    300        7.4696             nan     0.0100    0.0115
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.2548             nan     0.0100    0.8061
##      2       61.4699             nan     0.0100    0.7915
##      3       60.7402             nan     0.0100    0.7577
##      4       59.8992             nan     0.0100    0.7334
##      5       59.1060             nan     0.0100    0.7529
##      6       58.3326             nan     0.0100    0.7964
##      7       57.6439             nan     0.0100    0.6765
##      8       56.8926             nan     0.0100    0.7111
##      9       56.2014             nan     0.0100    0.5897
##     10       55.4997             nan     0.0100    0.6717
##     20       49.5355             nan     0.0100    0.5851
##     40       40.0924             nan     0.0100    0.3333
##     60       32.7896             nan     0.0100    0.2942
##     80       27.3288             nan     0.0100    0.2141
##    100       23.0735             nan     0.0100    0.1567
##    120       19.6550             nan     0.0100    0.1192
##    140       16.8807             nan     0.0100    0.0819
##    160       14.7496             nan     0.0100    0.0874
##    180       13.0081             nan     0.0100    0.0522
##    200       11.6161             nan     0.0100    0.0438
##    220       10.4591             nan     0.0100    0.0441
##    240        9.4867             nan     0.0100    0.0511
##    260        8.6952             nan     0.0100    0.0252
##    280        7.9925             nan     0.0100    0.0242
##    300        7.3941             nan     0.0100    0.0219
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.2376             nan     0.0100    0.7897
##      2       61.4274             nan     0.0100    0.7131
##      3       60.6555             nan     0.0100    0.7738
##      4       59.9434             nan     0.0100    0.6974
##      5       59.3473             nan     0.0100    0.5464
##      6       58.6124             nan     0.0100    0.7491
##      7       57.8910             nan     0.0100    0.7126
##      8       57.1892             nan     0.0100    0.7504
##      9       56.4092             nan     0.0100    0.7313
##     10       55.7567             nan     0.0100    0.6811
##     20       49.6541             nan     0.0100    0.5336
##     40       40.1147             nan     0.0100    0.3611
##     60       32.7073             nan     0.0100    0.3111
##     80       27.1754             nan     0.0100    0.2129
##    100       22.8383             nan     0.0100    0.1725
##    120       19.5164             nan     0.0100    0.1147
##    140       16.8971             nan     0.0100    0.1019
##    160       14.7879             nan     0.0100    0.0870
##    180       13.0736             nan     0.0100    0.0699
##    200       11.6870             nan     0.0100    0.0612
##    220       10.5845             nan     0.0100    0.0392
##    240        9.6453             nan     0.0100    0.0359
##    260        8.8397             nan     0.0100    0.0334
##    280        8.1239             nan     0.0100    0.0272
##    300        7.5593             nan     0.0100    0.0132
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.9539             nan     0.0100    1.1668
##      2       60.9824             nan     0.0100    0.9683
##      3       59.9876             nan     0.0100    0.8909
##      4       59.0259             nan     0.0100    0.9880
##      5       58.0166             nan     0.0100    1.0308
##      6       57.0378             nan     0.0100    0.9104
##      7       56.0812             nan     0.0100    0.9204
##      8       55.1884             nan     0.0100    0.8286
##      9       54.3192             nan     0.0100    0.8596
##     10       53.4327             nan     0.0100    0.8219
##     20       45.3861             nan     0.0100    0.6761
##     40       33.2536             nan     0.0100    0.5033
##     60       24.8681             nan     0.0100    0.3638
##     80       18.9626             nan     0.0100    0.2326
##    100       14.7304             nan     0.0100    0.1699
##    120       11.7940             nan     0.0100    0.1113
##    140        9.6475             nan     0.0100    0.0868
##    160        7.9975             nan     0.0100    0.0668
##    180        6.7912             nan     0.0100    0.0385
##    200        5.8523             nan     0.0100    0.0371
##    220        5.1535             nan     0.0100    0.0173
##    240        4.6072             nan     0.0100    0.0190
##    260        4.1991             nan     0.0100    0.0108
##    280        3.8961             nan     0.0100    0.0095
##    300        3.6450             nan     0.0100    0.0024
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.0046             nan     0.0100    1.0918
##      2       61.0094             nan     0.0100    0.9332
##      3       59.9559             nan     0.0100    0.9915
##      4       58.9444             nan     0.0100    1.0314
##      5       57.9191             nan     0.0100    1.0016
##      6       56.9410             nan     0.0100    0.9067
##      7       55.9738             nan     0.0100    0.9345
##      8       55.0426             nan     0.0100    0.8482
##      9       54.1755             nan     0.0100    0.8576
##     10       53.2549             nan     0.0100    0.8931
##     20       45.3461             nan     0.0100    0.6892
##     40       33.2212             nan     0.0100    0.4885
##     60       24.7630             nan     0.0100    0.3688
##     80       18.8274             nan     0.0100    0.2268
##    100       14.6583             nan     0.0100    0.1837
##    120       11.7113             nan     0.0100    0.1153
##    140        9.5935             nan     0.0100    0.0802
##    160        7.9960             nan     0.0100    0.0660
##    180        6.8114             nan     0.0100    0.0391
##    200        5.9353             nan     0.0100    0.0322
##    220        5.2488             nan     0.0100    0.0202
##    240        4.7346             nan     0.0100    0.0123
##    260        4.3508             nan     0.0100    0.0105
##    280        4.0461             nan     0.0100    0.0071
##    300        3.8115             nan     0.0100    0.0040
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.0001             nan     0.0100    1.0740
##      2       60.9038             nan     0.0100    1.1083
##      3       59.9266             nan     0.0100    0.9264
##      4       59.0104             nan     0.0100    0.9228
##      5       58.0411             nan     0.0100    0.9830
##      6       57.0756             nan     0.0100    1.0119
##      7       56.0865             nan     0.0100    0.8535
##      8       55.1521             nan     0.0100    1.0303
##      9       54.2604             nan     0.0100    0.8890
##     10       53.3742             nan     0.0100    0.8876
##     20       45.3899             nan     0.0100    0.7436
##     40       33.4799             nan     0.0100    0.5031
##     60       24.9248             nan     0.0100    0.3397
##     80       18.9729             nan     0.0100    0.2201
##    100       14.8811             nan     0.0100    0.1648
##    120       11.9465             nan     0.0100    0.1118
##    140        9.8061             nan     0.0100    0.0858
##    160        8.1776             nan     0.0100    0.0678
##    180        6.9984             nan     0.0100    0.0412
##    200        6.1040             nan     0.0100    0.0229
##    220        5.4288             nan     0.0100    0.0285
##    240        4.9209             nan     0.0100    0.0114
##    260        4.5227             nan     0.0100    0.0147
##    280        4.2334             nan     0.0100    0.0076
##    300        4.0007             nan     0.0100    0.0035
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.9837             nan     0.0100    1.2660
##      2       60.8948             nan     0.0100    1.0841
##      3       59.8178             nan     0.0100    1.0196
##      4       58.7476             nan     0.0100    1.0885
##      5       57.7419             nan     0.0100    0.9799
##      6       56.6968             nan     0.0100    0.9575
##      7       55.6822             nan     0.0100    0.9727
##      8       54.7111             nan     0.0100    0.8663
##      9       53.7506             nan     0.0100    1.0013
##     10       52.8013             nan     0.0100    0.8626
##     20       44.3519             nan     0.0100    0.7802
##     40       31.7695             nan     0.0100    0.4407
##     60       23.1116             nan     0.0100    0.3589
##     80       17.1328             nan     0.0100    0.2116
##    100       12.9814             nan     0.0100    0.1636
##    120       10.0087             nan     0.0100    0.1206
##    140        7.9274             nan     0.0100    0.0700
##    160        6.4625             nan     0.0100    0.0539
##    180        5.4034             nan     0.0100    0.0401
##    200        4.6191             nan     0.0100    0.0308
##    220        4.0379             nan     0.0100    0.0180
##    240        3.6145             nan     0.0100    0.0107
##    260        3.2971             nan     0.0100    0.0077
##    280        3.0615             nan     0.0100    0.0058
##    300        2.8623             nan     0.0100    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.9774             nan     0.0100    1.0771
##      2       60.8538             nan     0.0100    0.9865
##      3       59.7732             nan     0.0100    1.1052
##      4       58.7242             nan     0.0100    0.9586
##      5       57.7486             nan     0.0100    0.9731
##      6       56.7873             nan     0.0100    0.9770
##      7       55.8306             nan     0.0100    0.9614
##      8       54.8646             nan     0.0100    0.9977
##      9       53.9152             nan     0.0100    1.0610
##     10       52.9340             nan     0.0100    0.8258
##     20       44.5457             nan     0.0100    0.7060
##     40       32.0156             nan     0.0100    0.4970
##     60       23.2883             nan     0.0100    0.3717
##     80       17.2602             nan     0.0100    0.2312
##    100       12.9955             nan     0.0100    0.1558
##    120       10.0678             nan     0.0100    0.0866
##    140        8.0146             nan     0.0100    0.0738
##    160        6.5738             nan     0.0100    0.0539
##    180        5.5549             nan     0.0100    0.0431
##    200        4.7982             nan     0.0100    0.0277
##    220        4.2543             nan     0.0100    0.0086
##    240        3.8395             nan     0.0100    0.0132
##    260        3.5265             nan     0.0100    0.0030
##    280        3.2940             nan     0.0100    0.0060
##    300        3.0925             nan     0.0100    0.0046
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.9154             nan     0.0100    1.1238
##      2       60.8149             nan     0.0100    1.1661
##      3       59.7617             nan     0.0100    1.1066
##      4       58.7571             nan     0.0100    0.9320
##      5       57.7178             nan     0.0100    0.9879
##      6       56.7076             nan     0.0100    1.0182
##      7       55.7295             nan     0.0100    0.8906
##      8       54.7830             nan     0.0100    0.9281
##      9       53.7979             nan     0.0100    0.9521
##     10       52.8848             nan     0.0100    0.9026
##     20       44.3568             nan     0.0100    0.7371
##     40       31.8351             nan     0.0100    0.5580
##     60       23.2002             nan     0.0100    0.2975
##     80       17.3519             nan     0.0100    0.2460
##    100       13.2818             nan     0.0100    0.1677
##    120       10.3823             nan     0.0100    0.1282
##    140        8.3679             nan     0.0100    0.0746
##    160        6.9204             nan     0.0100    0.0556
##    180        5.8913             nan     0.0100    0.0357
##    200        5.1662             nan     0.0100    0.0266
##    220        4.6272             nan     0.0100    0.0134
##    240        4.2216             nan     0.0100    0.0055
##    260        3.9407             nan     0.0100    0.0066
##    280        3.7110             nan     0.0100    0.0042
##    300        3.5175             nan     0.0100    0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.3956             nan     0.0500    3.8244
##      2       55.6653             nan     0.0500    3.5007
##      3       52.5359             nan     0.0500    3.4368
##      4       49.7533             nan     0.0500    2.9200
##      5       46.9020             nan     0.0500    2.5685
##      6       44.4170             nan     0.0500    2.2151
##      7       41.7859             nan     0.0500    2.7660
##      8       39.8121             nan     0.0500    1.8870
##      9       37.6411             nan     0.0500    1.9385
##     10       35.6523             nan     0.0500    1.9580
##     20       22.8008             nan     0.0500    0.9617
##     40       11.8900             nan     0.0500    0.3024
##     60        7.5376             nan     0.0500    0.1444
##     80        5.5703             nan     0.0500    0.0462
##    100        4.5750             nan     0.0500    0.0356
##    120        4.0844             nan     0.0500   -0.0111
##    140        3.8096             nan     0.0500   -0.0011
##    160        3.6646             nan     0.0500   -0.0071
##    180        3.5481             nan     0.0500   -0.0235
##    200        3.4625             nan     0.0500   -0.0069
##    220        3.3860             nan     0.0500   -0.0044
##    240        3.3084             nan     0.0500   -0.0041
##    260        3.2543             nan     0.0500   -0.0039
##    280        3.2087             nan     0.0500   -0.0103
##    300        3.1608             nan     0.0500   -0.0035
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.3533             nan     0.0500    4.0646
##      2       55.7381             nan     0.0500    3.6016
##      3       52.2986             nan     0.0500    2.9537
##      4       49.0737             nan     0.0500    2.8889
##      5       46.4285             nan     0.0500    2.6391
##      6       43.8298             nan     0.0500    2.2559
##      7       41.4434             nan     0.0500    2.0393
##      8       39.3361             nan     0.0500    1.8964
##      9       37.4128             nan     0.0500    1.8635
##     10       35.5320             nan     0.0500    1.5653
##     20       22.3762             nan     0.0500    0.6353
##     40       11.4130             nan     0.0500    0.2955
##     60        7.3123             nan     0.0500    0.1036
##     80        5.5370             nan     0.0500    0.0393
##    100        4.5894             nan     0.0500    0.0257
##    120        4.1180             nan     0.0500   -0.0089
##    140        3.8594             nan     0.0500   -0.0062
##    160        3.7206             nan     0.0500   -0.0059
##    180        3.6166             nan     0.0500   -0.0042
##    200        3.5120             nan     0.0500   -0.0006
##    220        3.4236             nan     0.0500   -0.0028
##    240        3.3525             nan     0.0500   -0.0119
##    260        3.2847             nan     0.0500   -0.0046
##    280        3.2129             nan     0.0500   -0.0060
##    300        3.1649             nan     0.0500   -0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.1588             nan     0.0500    3.8371
##      2       55.3994             nan     0.0500    3.4684
##      3       52.2227             nan     0.0500    2.8593
##      4       49.1109             nan     0.0500    2.8627
##      5       46.3233             nan     0.0500    2.5798
##      6       44.1209             nan     0.0500    2.1129
##      7       41.6351             nan     0.0500    2.3346
##      8       39.7039             nan     0.0500    2.0924
##      9       37.3696             nan     0.0500    1.8478
##     10       35.4331             nan     0.0500    1.4631
##     20       22.3967             nan     0.0500    0.9002
##     40       11.4412             nan     0.0500    0.3380
##     60        7.4127             nan     0.0500    0.1385
##     80        5.6496             nan     0.0500    0.0358
##    100        4.8217             nan     0.0500    0.0150
##    120        4.4031             nan     0.0500    0.0026
##    140        4.1762             nan     0.0500    0.0078
##    160        4.0230             nan     0.0500    0.0039
##    180        3.9136             nan     0.0500   -0.0075
##    200        3.7983             nan     0.0500   -0.0005
##    220        3.7045             nan     0.0500   -0.0007
##    240        3.6205             nan     0.0500    0.0013
##    260        3.5399             nan     0.0500   -0.0111
##    280        3.4428             nan     0.0500   -0.0072
##    300        3.3816             nan     0.0500    0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.8096             nan     0.0500    4.5339
##      2       53.2912             nan     0.0500    4.8666
##      3       49.0263             nan     0.0500    4.0866
##      4       45.0731             nan     0.0500    3.6521
##      5       41.7066             nan     0.0500    3.4011
##      6       38.5323             nan     0.0500    2.9258
##      7       35.5658             nan     0.0500    2.9457
##      8       32.8483             nan     0.0500    2.6071
##      9       30.4466             nan     0.0500    2.4743
##     10       28.1842             nan     0.0500    2.3287
##     20       14.4072             nan     0.0500    0.8618
##     40        5.6984             nan     0.0500    0.1395
##     60        3.6580             nan     0.0500    0.0217
##     80        2.9315             nan     0.0500    0.0156
##    100        2.5774             nan     0.0500    0.0003
##    120        2.3260             nan     0.0500   -0.0031
##    140        2.1214             nan     0.0500   -0.0151
##    160        1.9488             nan     0.0500   -0.0079
##    180        1.8195             nan     0.0500   -0.0107
##    200        1.7380             nan     0.0500   -0.0136
##    220        1.6520             nan     0.0500   -0.0037
##    240        1.5563             nan     0.0500   -0.0117
##    260        1.4752             nan     0.0500   -0.0059
##    280        1.3987             nan     0.0500   -0.0109
##    300        1.3375             nan     0.0500   -0.0120
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.0403             nan     0.0500    4.9046
##      2       53.6307             nan     0.0500    4.7823
##      3       49.2229             nan     0.0500    4.6157
##      4       45.3146             nan     0.0500    3.9865
##      5       41.5877             nan     0.0500    3.4740
##      6       38.4794             nan     0.0500    3.3244
##      7       35.5854             nan     0.0500    2.7736
##      8       32.7445             nan     0.0500    2.3901
##      9       30.5538             nan     0.0500    2.2682
##     10       28.3767             nan     0.0500    2.3250
##     20       14.6102             nan     0.0500    0.8099
##     40        5.9244             nan     0.0500    0.1538
##     60        3.8559             nan     0.0500    0.0277
##     80        3.1458             nan     0.0500   -0.0006
##    100        2.7893             nan     0.0500    0.0020
##    120        2.5468             nan     0.0500   -0.0062
##    140        2.3860             nan     0.0500   -0.0029
##    160        2.2235             nan     0.0500   -0.0117
##    180        2.0948             nan     0.0500   -0.0013
##    200        1.9998             nan     0.0500   -0.0088
##    220        1.9197             nan     0.0500   -0.0115
##    240        1.8368             nan     0.0500   -0.0134
##    260        1.7651             nan     0.0500   -0.0107
##    280        1.7017             nan     0.0500   -0.0059
##    300        1.6435             nan     0.0500   -0.0104
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.7374             nan     0.0500    6.0841
##      2       53.0715             nan     0.0500    4.4158
##      3       48.9888             nan     0.0500    4.3379
##      4       45.1731             nan     0.0500    3.8862
##      5       41.6098             nan     0.0500    3.7575
##      6       38.2443             nan     0.0500    2.9238
##      7       35.5846             nan     0.0500    2.6468
##      8       32.8897             nan     0.0500    2.6699
##      9       30.4454             nan     0.0500    2.4746
##     10       28.2602             nan     0.0500    2.1774
##     20       14.7010             nan     0.0500    0.8192
##     40        6.0886             nan     0.0500    0.1375
##     60        4.0019             nan     0.0500    0.0443
##     80        3.3635             nan     0.0500   -0.0001
##    100        3.0289             nan     0.0500   -0.0185
##    120        2.7856             nan     0.0500   -0.0129
##    140        2.5755             nan     0.0500   -0.0099
##    160        2.4370             nan     0.0500   -0.0085
##    180        2.3030             nan     0.0500   -0.0055
##    200        2.1950             nan     0.0500   -0.0146
##    220        2.0819             nan     0.0500   -0.0065
##    240        1.9920             nan     0.0500   -0.0106
##    260        1.9206             nan     0.0500   -0.0125
##    280        1.8515             nan     0.0500   -0.0042
##    300        1.7872             nan     0.0500   -0.0075
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.3901             nan     0.0500    5.9261
##      2       52.7396             nan     0.0500    4.9300
##      3       48.0968             nan     0.0500    4.5091
##      4       44.0206             nan     0.0500    3.8374
##      5       40.3544             nan     0.0500    3.9691
##      6       36.8693             nan     0.0500    3.4354
##      7       33.7800             nan     0.0500    3.0243
##      8       30.9552             nan     0.0500    2.6098
##      9       28.5795             nan     0.0500    2.3263
##     10       26.3316             nan     0.0500    2.1134
##     20       12.5268             nan     0.0500    0.8300
##     40        4.5924             nan     0.0500    0.1748
##     60        2.9367             nan     0.0500   -0.0016
##     80        2.3302             nan     0.0500   -0.0138
##    100        2.0168             nan     0.0500   -0.0226
##    120        1.7391             nan     0.0500   -0.0108
##    140        1.5756             nan     0.0500   -0.0085
##    160        1.4201             nan     0.0500   -0.0081
##    180        1.2799             nan     0.0500   -0.0068
##    200        1.1660             nan     0.0500   -0.0067
##    220        1.0653             nan     0.0500   -0.0126
##    240        0.9907             nan     0.0500   -0.0103
##    260        0.9093             nan     0.0500   -0.0069
##    280        0.8416             nan     0.0500   -0.0059
##    300        0.7864             nan     0.0500   -0.0095
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.8166             nan     0.0500    5.4690
##      2       52.9766             nan     0.0500    4.7273
##      3       48.5490             nan     0.0500    3.9240
##      4       44.3757             nan     0.0500    3.8684
##      5       40.6733             nan     0.0500    3.3667
##      6       37.1948             nan     0.0500    3.4309
##      7       34.1868             nan     0.0500    2.8596
##      8       31.4257             nan     0.0500    2.5529
##      9       28.8096             nan     0.0500    2.2876
##     10       26.4545             nan     0.0500    2.1163
##     20       12.6418             nan     0.0500    0.8235
##     40        4.6435             nan     0.0500    0.1253
##     60        3.0974             nan     0.0500    0.0176
##     80        2.5902             nan     0.0500   -0.0099
##    100        2.2615             nan     0.0500    0.0047
##    120        2.0305             nan     0.0500   -0.0127
##    140        1.8496             nan     0.0500   -0.0063
##    160        1.7091             nan     0.0500   -0.0193
##    180        1.5882             nan     0.0500   -0.0121
##    200        1.4789             nan     0.0500   -0.0049
##    220        1.3840             nan     0.0500   -0.0060
##    240        1.2883             nan     0.0500   -0.0091
##    260        1.2151             nan     0.0500   -0.0124
##    280        1.1527             nan     0.0500   -0.0075
##    300        1.0926             nan     0.0500   -0.0072
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.8439             nan     0.0500    5.4330
##      2       53.0236             nan     0.0500    5.1841
##      3       48.7113             nan     0.0500    4.0321
##      4       44.5724             nan     0.0500    3.9382
##      5       40.8458             nan     0.0500    3.9470
##      6       37.5173             nan     0.0500    2.8976
##      7       34.3400             nan     0.0500    3.1000
##      8       31.5673             nan     0.0500    2.5679
##      9       29.1831             nan     0.0500    2.3236
##     10       26.8846             nan     0.0500    2.1696
##     20       13.1125             nan     0.0500    0.8076
##     40        5.1167             nan     0.0500    0.1116
##     60        3.5455             nan     0.0500    0.0092
##     80        2.9536             nan     0.0500   -0.0039
##    100        2.6051             nan     0.0500   -0.0036
##    120        2.3859             nan     0.0500   -0.0082
##    140        2.2062             nan     0.0500   -0.0117
##    160        2.0454             nan     0.0500   -0.0075
##    180        1.9187             nan     0.0500   -0.0103
##    200        1.8172             nan     0.0500   -0.0137
##    220        1.7101             nan     0.0500   -0.0039
##    240        1.6247             nan     0.0500   -0.0143
##    260        1.5270             nan     0.0500   -0.0049
##    280        1.4606             nan     0.0500   -0.0197
##    300        1.3837             nan     0.0500   -0.0090
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.1438             nan     0.1000    7.6410
##      2       49.6843             nan     0.1000    6.0802
##      3       43.7805             nan     0.1000    5.6168
##      4       39.0668             nan     0.1000    3.9554
##      5       35.4015             nan     0.1000    3.2502
##      6       31.6470             nan     0.1000    3.5191
##      7       28.8782             nan     0.1000    2.7394
##      8       26.1881             nan     0.1000    2.3165
##      9       23.8081             nan     0.1000    1.9816
##     10       21.5368             nan     0.1000    2.2400
##     20       11.1150             nan     0.1000    0.4144
##     40        5.4556             nan     0.1000    0.0768
##     60        4.1872             nan     0.1000    0.0067
##     80        3.8071             nan     0.1000   -0.0298
##    100        3.5672             nan     0.1000   -0.0086
##    120        3.4321             nan     0.1000   -0.0254
##    140        3.2762             nan     0.1000   -0.0119
##    160        3.1609             nan     0.1000   -0.0123
##    180        3.0841             nan     0.1000   -0.0098
##    200        3.0077             nan     0.1000   -0.0052
##    220        2.9371             nan     0.1000   -0.0359
##    240        2.8913             nan     0.1000   -0.0215
##    260        2.8233             nan     0.1000   -0.0148
##    280        2.7803             nan     0.1000   -0.0066
##    300        2.7299             nan     0.1000   -0.0101
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.8849             nan     0.1000    7.5552
##      2       49.8578             nan     0.1000    5.6390
##      3       44.5300             nan     0.1000    5.7536
##      4       40.2391             nan     0.1000    4.3531
##      5       35.9313             nan     0.1000    3.9622
##      6       32.4866             nan     0.1000    3.3353
##      7       29.1851             nan     0.1000    2.8554
##      8       26.4746             nan     0.1000    2.5684
##      9       24.0476             nan     0.1000    2.1929
##     10       22.2728             nan     0.1000    1.4910
##     20       11.0843             nan     0.1000    0.4982
##     40        5.4835             nan     0.1000    0.0019
##     60        4.2099             nan     0.1000    0.0249
##     80        3.8625             nan     0.1000   -0.0256
##    100        3.6130             nan     0.1000   -0.0086
##    120        3.4371             nan     0.1000   -0.0130
##    140        3.2897             nan     0.1000   -0.0045
##    160        3.1796             nan     0.1000   -0.0057
##    180        3.1010             nan     0.1000   -0.0033
##    200        3.0279             nan     0.1000   -0.0114
##    220        2.9605             nan     0.1000   -0.0274
##    240        2.8913             nan     0.1000   -0.0037
##    260        2.8233             nan     0.1000   -0.0040
##    280        2.7636             nan     0.1000   -0.0080
##    300        2.7211             nan     0.1000   -0.0071
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.9434             nan     0.1000    7.1297
##      2       49.3077             nan     0.1000    5.8306
##      3       43.7682             nan     0.1000    5.3651
##      4       39.4861             nan     0.1000    4.3868
##      5       35.4206             nan     0.1000    4.1375
##      6       31.8842             nan     0.1000    3.0288
##      7       28.9768             nan     0.1000    2.6172
##      8       26.6642             nan     0.1000    2.3271
##      9       24.1922             nan     0.1000    2.3758
##     10       22.1891             nan     0.1000    1.9361
##     20       11.4506             nan     0.1000    0.5568
##     40        5.6318             nan     0.1000    0.1027
##     60        4.4680             nan     0.1000    0.0143
##     80        4.0773             nan     0.1000   -0.0093
##    100        3.8534             nan     0.1000   -0.0392
##    120        3.6556             nan     0.1000   -0.0076
##    140        3.5054             nan     0.1000   -0.0039
##    160        3.3528             nan     0.1000    0.0001
##    180        3.2504             nan     0.1000   -0.0084
##    200        3.1698             nan     0.1000   -0.0244
##    220        3.0757             nan     0.1000   -0.0038
##    240        3.0177             nan     0.1000   -0.0069
##    260        2.9442             nan     0.1000   -0.0103
##    280        2.8672             nan     0.1000   -0.0272
##    300        2.7861             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.7412             nan     0.1000    8.9841
##      2       44.3088             nan     0.1000    7.7404
##      3       37.1985             nan     0.1000    6.9160
##      4       31.9109             nan     0.1000    5.3672
##      5       27.2800             nan     0.1000    4.0731
##      6       23.2697             nan     0.1000    3.5761
##      7       20.2830             nan     0.1000    3.1753
##      8       17.8072             nan     0.1000    2.3982
##      9       15.6642             nan     0.1000    1.8860
##     10       13.7190             nan     0.1000    1.9197
##     20        5.5605             nan     0.1000    0.3615
##     40        3.1109             nan     0.1000    0.0152
##     60        2.5465             nan     0.1000   -0.0117
##     80        2.1519             nan     0.1000   -0.0449
##    100        1.8751             nan     0.1000   -0.0148
##    120        1.7096             nan     0.1000   -0.0138
##    140        1.5726             nan     0.1000   -0.0331
##    160        1.4501             nan     0.1000   -0.0174
##    180        1.3180             nan     0.1000   -0.0102
##    200        1.1941             nan     0.1000   -0.0185
##    220        1.1013             nan     0.1000   -0.0145
##    240        1.0137             nan     0.1000   -0.0221
##    260        0.9558             nan     0.1000   -0.0062
##    280        0.8985             nan     0.1000   -0.0130
##    300        0.8435             nan     0.1000   -0.0068
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.3344             nan     0.1000    9.9743
##      2       45.0409             nan     0.1000    8.3071
##      3       38.1850             nan     0.1000    6.7013
##      4       32.6666             nan     0.1000    5.0264
##      5       27.8562             nan     0.1000    4.4839
##      6       24.1889             nan     0.1000    3.7144
##      7       20.9650             nan     0.1000    3.1286
##      8       18.5104             nan     0.1000    2.4828
##      9       16.2899             nan     0.1000    2.2134
##     10       14.4691             nan     0.1000    1.5834
##     20        5.8636             nan     0.1000    0.3076
##     40        3.1876             nan     0.1000   -0.0456
##     60        2.6828             nan     0.1000   -0.0121
##     80        2.2741             nan     0.1000   -0.0360
##    100        2.0673             nan     0.1000   -0.0053
##    120        1.8775             nan     0.1000   -0.0226
##    140        1.7333             nan     0.1000   -0.0471
##    160        1.6139             nan     0.1000   -0.0163
##    180        1.5099             nan     0.1000   -0.0066
##    200        1.4060             nan     0.1000   -0.0238
##    220        1.3298             nan     0.1000   -0.0185
##    240        1.2528             nan     0.1000   -0.0123
##    260        1.1764             nan     0.1000   -0.0145
##    280        1.1199             nan     0.1000   -0.0250
##    300        1.0599             nan     0.1000   -0.0105
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.0915             nan     0.1000   10.5705
##      2       45.0014             nan     0.1000    8.4517
##      3       37.9915             nan     0.1000    6.7091
##      4       32.4900             nan     0.1000    4.9277
##      5       27.9204             nan     0.1000    4.3298
##      6       23.9782             nan     0.1000    3.6528
##      7       20.8991             nan     0.1000    2.9252
##      8       18.2168             nan     0.1000    2.6738
##      9       15.9633             nan     0.1000    2.0135
##     10       14.1448             nan     0.1000    1.7032
##     20        5.9339             nan     0.1000    0.3635
##     40        3.4261             nan     0.1000    0.0246
##     60        2.8715             nan     0.1000   -0.0046
##     80        2.4889             nan     0.1000   -0.0150
##    100        2.2028             nan     0.1000   -0.0221
##    120        2.0199             nan     0.1000   -0.0183
##    140        1.8795             nan     0.1000   -0.0098
##    160        1.7380             nan     0.1000   -0.0168
##    180        1.6384             nan     0.1000   -0.0176
##    200        1.5427             nan     0.1000   -0.0064
##    220        1.4781             nan     0.1000   -0.0076
##    240        1.3925             nan     0.1000   -0.0115
##    260        1.3170             nan     0.1000   -0.0182
##    280        1.2523             nan     0.1000   -0.0174
##    300        1.2009             nan     0.1000   -0.0113
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.8281             nan     0.1000    9.3216
##      2       44.3513             nan     0.1000    7.7461
##      3       36.9729             nan     0.1000    6.8963
##      4       31.1202             nan     0.1000    5.2217
##      5       26.5263             nan     0.1000    4.6090
##      6       22.4745             nan     0.1000    3.6961
##      7       19.4208             nan     0.1000    3.2717
##      8       16.6406             nan     0.1000    2.5089
##      9       14.3394             nan     0.1000    2.0172
##     10       12.4630             nan     0.1000    1.2673
##     20        4.4724             nan     0.1000    0.2713
##     40        2.3520             nan     0.1000    0.0099
##     60        1.7759             nan     0.1000   -0.0302
##     80        1.4284             nan     0.1000   -0.0179
##    100        1.2134             nan     0.1000   -0.0193
##    120        1.0223             nan     0.1000   -0.0267
##    140        0.8623             nan     0.1000   -0.0044
##    160        0.7468             nan     0.1000   -0.0131
##    180        0.6454             nan     0.1000   -0.0061
##    200        0.5733             nan     0.1000   -0.0134
##    220        0.5096             nan     0.1000   -0.0125
##    240        0.4575             nan     0.1000   -0.0118
##    260        0.4090             nan     0.1000   -0.0093
##    280        0.3661             nan     0.1000   -0.0099
##    300        0.3285             nan     0.1000   -0.0085
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.9475             nan     0.1000    9.9414
##      2       44.3752             nan     0.1000    7.5275
##      3       37.1191             nan     0.1000    7.2150
##      4       31.2794             nan     0.1000    5.8318
##      5       26.3339             nan     0.1000    4.9722
##      6       22.3651             nan     0.1000    3.9869
##      7       19.0259             nan     0.1000    3.2341
##      8       16.5154             nan     0.1000    2.3179
##      9       14.4399             nan     0.1000    2.2921
##     10       12.6663             nan     0.1000    1.6430
##     20        4.6739             nan     0.1000    0.2106
##     40        2.6628             nan     0.1000   -0.0120
##     60        2.1095             nan     0.1000   -0.0118
##     80        1.8062             nan     0.1000   -0.0316
##    100        1.5762             nan     0.1000   -0.0224
##    120        1.3809             nan     0.1000   -0.0215
##    140        1.2163             nan     0.1000   -0.0200
##    160        1.0979             nan     0.1000   -0.0126
##    180        0.9896             nan     0.1000   -0.0176
##    200        0.8953             nan     0.1000   -0.0109
##    220        0.8216             nan     0.1000   -0.0189
##    240        0.7489             nan     0.1000   -0.0143
##    260        0.6933             nan     0.1000   -0.0114
##    280        0.6449             nan     0.1000   -0.0115
##    300        0.5809             nan     0.1000   -0.0081
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.2363             nan     0.1000   11.4975
##      2       43.7069             nan     0.1000    8.8512
##      3       36.6773             nan     0.1000    6.5151
##      4       30.7827             nan     0.1000    5.4660
##      5       26.2092             nan     0.1000    4.3566
##      6       22.4510             nan     0.1000    3.9423
##      7       19.2072             nan     0.1000    3.1462
##      8       16.8900             nan     0.1000    2.1972
##      9       14.5968             nan     0.1000    2.2840
##     10       12.9082             nan     0.1000    1.7076
##     20        5.0341             nan     0.1000    0.2392
##     40        3.0711             nan     0.1000    0.0147
##     60        2.5345             nan     0.1000   -0.0565
##     80        2.2039             nan     0.1000   -0.0094
##    100        1.9339             nan     0.1000   -0.0273
##    120        1.7305             nan     0.1000   -0.0242
##    140        1.5635             nan     0.1000   -0.0267
##    160        1.4299             nan     0.1000   -0.0267
##    180        1.3241             nan     0.1000   -0.0262
##    200        1.2199             nan     0.1000   -0.0199
##    220        1.1367             nan     0.1000   -0.0131
##    240        1.0470             nan     0.1000   -0.0157
##    260        0.9730             nan     0.1000   -0.0071
##    280        0.9186             nan     0.1000   -0.0094
##    300        0.8576             nan     0.1000   -0.0109
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.4123             nan     0.0100    0.7552
##      2       60.6847             nan     0.0100    0.7746
##      3       59.9616             nan     0.0100    0.7510
##      4       59.2769             nan     0.0100    0.7578
##      5       58.5815             nan     0.0100    0.7104
##      6       57.8613             nan     0.0100    0.7247
##      7       57.1910             nan     0.0100    0.7041
##      8       56.5057             nan     0.0100    0.6832
##      9       55.8467             nan     0.0100    0.6842
##     10       55.1497             nan     0.0100    0.6449
##     20       49.2898             nan     0.0100    0.4414
##     40       39.7083             nan     0.0100    0.4003
##     60       32.4471             nan     0.0100    0.2922
##     80       26.9294             nan     0.0100    0.2139
##    100       22.7901             nan     0.0100    0.1500
##    120       19.4928             nan     0.0100    0.1305
##    140       16.8889             nan     0.0100    0.1103
##    160       14.8397             nan     0.0100    0.0889
##    180       13.0977             nan     0.0100    0.0621
##    200       11.6923             nan     0.0100    0.0463
##    220       10.5519             nan     0.0100    0.0455
##    240        9.5875             nan     0.0100    0.0347
##    260        8.7787             nan     0.0100    0.0288
##    280        8.0850             nan     0.0100    0.0200
##    300        7.4825             nan     0.0100    0.0238
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.3597             nan     0.0100    0.7736
##      2       60.5635             nan     0.0100    0.7617
##      3       59.7441             nan     0.0100    0.7398
##      4       58.9336             nan     0.0100    0.7469
##      5       58.2203             nan     0.0100    0.7285
##      6       57.4655             nan     0.0100    0.7159
##      7       56.6781             nan     0.0100    0.6548
##      8       56.0807             nan     0.0100    0.6583
##      9       55.4060             nan     0.0100    0.6756
##     10       54.7378             nan     0.0100    0.6507
##     20       48.7677             nan     0.0100    0.5336
##     40       39.2732             nan     0.0100    0.3985
##     60       32.2951             nan     0.0100    0.3395
##     80       26.8741             nan     0.0100    0.2305
##    100       22.6560             nan     0.0100    0.1701
##    120       19.3996             nan     0.0100    0.1340
##    140       16.8076             nan     0.0100    0.1056
##    160       14.7149             nan     0.0100    0.0792
##    180       13.0342             nan     0.0100    0.0496
##    200       11.6096             nan     0.0100    0.0485
##    220       10.4521             nan     0.0100    0.0438
##    240        9.4651             nan     0.0100    0.0211
##    260        8.6537             nan     0.0100    0.0371
##    280        7.9659             nan     0.0100    0.0266
##    300        7.3938             nan     0.0100    0.0216
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.2880             nan     0.0100    0.8177
##      2       60.5290             nan     0.0100    0.7330
##      3       59.8133             nan     0.0100    0.7353
##      4       59.0411             nan     0.0100    0.7028
##      5       58.3450             nan     0.0100    0.6948
##      6       57.6646             nan     0.0100    0.6865
##      7       56.9669             nan     0.0100    0.7072
##      8       56.3224             nan     0.0100    0.6302
##      9       55.6263             nan     0.0100    0.6523
##     10       54.9537             nan     0.0100    0.6061
##     20       48.9212             nan     0.0100    0.5676
##     40       39.5169             nan     0.0100    0.3862
##     60       32.2676             nan     0.0100    0.3032
##     80       26.9242             nan     0.0100    0.2374
##    100       22.7466             nan     0.0100    0.1695
##    120       19.5099             nan     0.0100    0.1342
##    140       16.9995             nan     0.0100    0.1033
##    160       14.8824             nan     0.0100    0.0782
##    180       13.2226             nan     0.0100    0.0483
##    200       11.8360             nan     0.0100    0.0641
##    220       10.6695             nan     0.0100    0.0409
##    240        9.6687             nan     0.0100    0.0305
##    260        8.8797             nan     0.0100    0.0286
##    280        8.1734             nan     0.0100    0.0274
##    300        7.5685             nan     0.0100    0.0168
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.0977             nan     0.0100    0.9427
##      2       60.0594             nan     0.0100    1.0613
##      3       59.0918             nan     0.0100    1.0262
##      4       58.1146             nan     0.0100    0.8663
##      5       57.1321             nan     0.0100    0.9276
##      6       56.2013             nan     0.0100    0.9042
##      7       55.3087             nan     0.0100    0.9600
##      8       54.3788             nan     0.0100    0.9244
##      9       53.4878             nan     0.0100    0.8785
##     10       52.6386             nan     0.0100    0.9086
##     20       44.8154             nan     0.0100    0.7210
##     40       32.8350             nan     0.0100    0.4899
##     60       24.4946             nan     0.0100    0.3370
##     80       18.7028             nan     0.0100    0.2310
##    100       14.6219             nan     0.0100    0.1863
##    120       11.5693             nan     0.0100    0.1186
##    140        9.4305             nan     0.0100    0.0736
##    160        7.8682             nan     0.0100    0.0646
##    180        6.6711             nan     0.0100    0.0428
##    200        5.7766             nan     0.0100    0.0313
##    220        5.1078             nan     0.0100    0.0212
##    240        4.6183             nan     0.0100    0.0042
##    260        4.2265             nan     0.0100    0.0123
##    280        3.9328             nan     0.0100    0.0074
##    300        3.6914             nan     0.0100    0.0074
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.1732             nan     0.0100    0.9445
##      2       60.1957             nan     0.0100    0.9623
##      3       59.1990             nan     0.0100    0.8884
##      4       58.2403             nan     0.0100    0.9414
##      5       57.3062             nan     0.0100    0.8516
##      6       56.3683             nan     0.0100    0.8758
##      7       55.4381             nan     0.0100    0.9874
##      8       54.5488             nan     0.0100    0.8425
##      9       53.6730             nan     0.0100    0.8442
##     10       52.7985             nan     0.0100    0.8567
##     20       44.9028             nan     0.0100    0.6723
##     40       32.8287             nan     0.0100    0.5122
##     60       24.4930             nan     0.0100    0.3126
##     80       18.7801             nan     0.0100    0.2517
##    100       14.7229             nan     0.0100    0.1706
##    120       11.7417             nan     0.0100    0.1077
##    140        9.5099             nan     0.0100    0.0873
##    160        7.9306             nan     0.0100    0.0537
##    180        6.7694             nan     0.0100    0.0473
##    200        5.8805             nan     0.0100    0.0343
##    220        5.2281             nan     0.0100    0.0260
##    240        4.7385             nan     0.0100    0.0115
##    260        4.3735             nan     0.0100    0.0150
##    280        4.0978             nan     0.0100    0.0072
##    300        3.8860             nan     0.0100    0.0060
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.1186             nan     0.0100    1.0299
##      2       60.0822             nan     0.0100    0.9548
##      3       59.1150             nan     0.0100    0.9146
##      4       58.1126             nan     0.0100    1.0974
##      5       57.1264             nan     0.0100    0.8615
##      6       56.1828             nan     0.0100    0.8528
##      7       55.3212             nan     0.0100    0.8592
##      8       54.4465             nan     0.0100    0.8176
##      9       53.5818             nan     0.0100    0.8348
##     10       52.6712             nan     0.0100    0.9044
##     20       44.7625             nan     0.0100    0.7289
##     40       32.8066             nan     0.0100    0.4794
##     60       24.4810             nan     0.0100    0.3705
##     80       18.6729             nan     0.0100    0.1756
##    100       14.6467             nan     0.0100    0.1584
##    120       11.7640             nan     0.0100    0.1256
##    140        9.6319             nan     0.0100    0.0743
##    160        8.0970             nan     0.0100    0.0634
##    180        6.9714             nan     0.0100    0.0409
##    200        6.0629             nan     0.0100    0.0347
##    220        5.3935             nan     0.0100    0.0242
##    240        4.8824             nan     0.0100    0.0170
##    260        4.5032             nan     0.0100    0.0138
##    280        4.2157             nan     0.0100    0.0069
##    300        4.0079             nan     0.0100    0.0055
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.0181             nan     0.0100    1.1632
##      2       59.9494             nan     0.0100    0.9765
##      3       58.8971             nan     0.0100    0.9996
##      4       57.8735             nan     0.0100    1.0641
##      5       56.8328             nan     0.0100    1.0347
##      6       55.7976             nan     0.0100    0.9063
##      7       54.8582             nan     0.0100    0.9821
##      8       53.9091             nan     0.0100    0.8822
##      9       52.9542             nan     0.0100    0.9450
##     10       52.0128             nan     0.0100    0.9283
##     20       43.7734             nan     0.0100    0.7030
##     40       31.2970             nan     0.0100    0.5270
##     60       22.7000             nan     0.0100    0.3558
##     80       16.7886             nan     0.0100    0.2125
##    100       12.7738             nan     0.0100    0.1402
##    120        9.8961             nan     0.0100    0.1199
##    140        7.8415             nan     0.0100    0.0743
##    160        6.4162             nan     0.0100    0.0461
##    180        5.3918             nan     0.0100    0.0276
##    200        4.6298             nan     0.0100    0.0222
##    220        4.0768             nan     0.0100    0.0169
##    240        3.6884             nan     0.0100    0.0095
##    260        3.3903             nan     0.0100    0.0057
##    280        3.1447             nan     0.0100    0.0033
##    300        2.9599             nan     0.0100   -0.0021
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.0860             nan     0.0100    1.0078
##      2       59.9509             nan     0.0100    0.8955
##      3       58.8792             nan     0.0100    0.9906
##      4       57.8356             nan     0.0100    0.9603
##      5       56.8157             nan     0.0100    0.9592
##      6       55.8189             nan     0.0100    0.9664
##      7       54.8035             nan     0.0100    0.9628
##      8       53.8682             nan     0.0100    0.8291
##      9       52.9132             nan     0.0100    1.0020
##     10       51.9945             nan     0.0100    0.7970
##     20       43.8395             nan     0.0100    0.7837
##     40       31.5341             nan     0.0100    0.5718
##     60       22.8647             nan     0.0100    0.2933
##     80       17.0311             nan     0.0100    0.2382
##    100       12.9416             nan     0.0100    0.1413
##    120       10.1329             nan     0.0100    0.1129
##    140        8.1037             nan     0.0100    0.0748
##    160        6.6616             nan     0.0100    0.0520
##    180        5.5995             nan     0.0100    0.0487
##    200        4.8319             nan     0.0100    0.0249
##    220        4.2943             nan     0.0100    0.0135
##    240        3.8933             nan     0.0100    0.0162
##    260        3.6135             nan     0.0100    0.0003
##    280        3.3702             nan     0.0100    0.0020
##    300        3.1901             nan     0.0100   -0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       61.0997             nan     0.0100    1.1448
##      2       60.0816             nan     0.0100    1.0968
##      3       59.1176             nan     0.0100    1.0778
##      4       58.0978             nan     0.0100    1.0203
##      5       57.0813             nan     0.0100    1.0607
##      6       56.0957             nan     0.0100    0.9857
##      7       55.1123             nan     0.0100    0.9380
##      8       54.2166             nan     0.0100    0.9265
##      9       53.2753             nan     0.0100    0.8730
##     10       52.3434             nan     0.0100    0.9844
##     20       44.2088             nan     0.0100    0.6842
##     40       31.6618             nan     0.0100    0.5597
##     60       23.2728             nan     0.0100    0.3441
##     80       17.3936             nan     0.0100    0.2397
##    100       13.2763             nan     0.0100    0.1328
##    120       10.4387             nan     0.0100    0.1139
##    140        8.3681             nan     0.0100    0.0963
##    160        6.9513             nan     0.0100    0.0513
##    180        5.9267             nan     0.0100    0.0384
##    200        5.1747             nan     0.0100    0.0271
##    220        4.6475             nan     0.0100    0.0155
##    240        4.2646             nan     0.0100    0.0093
##    260        3.9615             nan     0.0100    0.0075
##    280        3.7386             nan     0.0100   -0.0005
##    300        3.5676             nan     0.0100   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.1881             nan     0.0500    3.8208
##      2       54.5427             nan     0.0500    3.4755
##      3       51.4488             nan     0.0500    3.0136
##      4       48.6118             nan     0.0500    3.0824
##      5       45.8034             nan     0.0500    2.7056
##      6       43.4423             nan     0.0500    2.1932
##      7       41.2264             nan     0.0500    2.2131
##      8       39.1029             nan     0.0500    1.7811
##      9       37.1368             nan     0.0500    2.2201
##     10       35.3039             nan     0.0500    1.6741
##     20       23.1465             nan     0.0500    0.7675
##     40       11.6412             nan     0.0500    0.3018
##     60        7.5487             nan     0.0500    0.0720
##     80        5.5792             nan     0.0500    0.0561
##    100        4.5669             nan     0.0500    0.0096
##    120        4.0937             nan     0.0500    0.0067
##    140        3.8254             nan     0.0500   -0.0052
##    160        3.6798             nan     0.0500   -0.0035
##    180        3.5808             nan     0.0500   -0.0064
##    200        3.4919             nan     0.0500   -0.0052
##    220        3.4185             nan     0.0500   -0.0022
##    240        3.3672             nan     0.0500   -0.0002
##    260        3.3115             nan     0.0500   -0.0156
##    280        3.2659             nan     0.0500   -0.0050
##    300        3.2192             nan     0.0500   -0.0146
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.0602             nan     0.0500    3.8358
##      2       54.4010             nan     0.0500    3.2404
##      3       51.3143             nan     0.0500    2.8677
##      4       48.0191             nan     0.0500    2.7982
##      5       45.3547             nan     0.0500    2.5488
##      6       43.0056             nan     0.0500    2.4537
##      7       40.5589             nan     0.0500    2.1190
##      8       38.4296             nan     0.0500    1.9977
##      9       36.2655             nan     0.0500    1.7318
##     10       34.5600             nan     0.0500    1.7133
##     20       22.3853             nan     0.0500    1.0161
##     40       11.7448             nan     0.0500    0.2635
##     60        7.4733             nan     0.0500    0.0369
##     80        5.5585             nan     0.0500    0.0248
##    100        4.6152             nan     0.0500    0.0273
##    120        4.1724             nan     0.0500    0.0042
##    140        3.9429             nan     0.0500   -0.0043
##    160        3.8223             nan     0.0500   -0.0179
##    180        3.7057             nan     0.0500   -0.0103
##    200        3.6266             nan     0.0500   -0.0099
##    220        3.5412             nan     0.0500   -0.0090
##    240        3.4828             nan     0.0500    0.0007
##    260        3.4252             nan     0.0500   -0.0005
##    280        3.3829             nan     0.0500   -0.0073
##    300        3.3251             nan     0.0500   -0.0038
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.2653             nan     0.0500    3.9766
##      2       54.8815             nan     0.0500    3.1078
##      3       51.8309             nan     0.0500    3.3556
##      4       48.7801             nan     0.0500    2.8763
##      5       46.2317             nan     0.0500    2.6096
##      6       43.7682             nan     0.0500    2.3220
##      7       41.3816             nan     0.0500    2.2582
##      8       39.2572             nan     0.0500    2.0708
##      9       37.0796             nan     0.0500    2.2355
##     10       35.2031             nan     0.0500    1.9405
##     20       22.6753             nan     0.0500    0.7557
##     40       12.0534             nan     0.0500    0.2027
##     60        7.6379             nan     0.0500    0.1136
##     80        5.7225             nan     0.0500    0.0519
##    100        4.8653             nan     0.0500   -0.0086
##    120        4.4377             nan     0.0500   -0.0108
##    140        4.2375             nan     0.0500    0.0003
##    160        4.1094             nan     0.0500   -0.0111
##    180        4.0002             nan     0.0500   -0.0061
##    200        3.9106             nan     0.0500   -0.0020
##    220        3.8325             nan     0.0500   -0.0031
##    240        3.7628             nan     0.0500   -0.0103
##    260        3.7043             nan     0.0500   -0.0033
##    280        3.6272             nan     0.0500   -0.0048
##    300        3.5592             nan     0.0500   -0.0094
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.0438             nan     0.0500    4.5934
##      2       52.4670             nan     0.0500    4.2808
##      3       48.1446             nan     0.0500    3.7412
##      4       44.4282             nan     0.0500    3.9592
##      5       40.9729             nan     0.0500    3.3759
##      6       37.9089             nan     0.0500    3.0781
##      7       35.0891             nan     0.0500    2.8219
##      8       32.4289             nan     0.0500    2.7141
##      9       29.8832             nan     0.0500    2.4005
##     10       27.6554             nan     0.0500    1.8928
##     20       14.3098             nan     0.0500    0.8156
##     40        5.8797             nan     0.0500    0.1527
##     60        3.7304             nan     0.0500    0.0196
##     80        3.1475             nan     0.0500   -0.0154
##    100        2.8373             nan     0.0500   -0.0043
##    120        2.6050             nan     0.0500   -0.0210
##    140        2.4566             nan     0.0500   -0.0091
##    160        2.3281             nan     0.0500   -0.0029
##    180        2.1866             nan     0.0500   -0.0067
##    200        2.0541             nan     0.0500   -0.0039
##    220        1.9187             nan     0.0500   -0.0061
##    240        1.8218             nan     0.0500   -0.0109
##    260        1.7311             nan     0.0500   -0.0104
##    280        1.6543             nan     0.0500   -0.0069
##    300        1.5843             nan     0.0500   -0.0087
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.8417             nan     0.0500    4.5411
##      2       52.3757             nan     0.0500    4.3217
##      3       48.1222             nan     0.0500    3.7584
##      4       44.3999             nan     0.0500    3.6053
##      5       41.0076             nan     0.0500    3.0017
##      6       37.8972             nan     0.0500    3.2062
##      7       35.0140             nan     0.0500    2.8904
##      8       32.3628             nan     0.0500    2.6887
##      9       29.9268             nan     0.0500    2.1858
##     10       27.7878             nan     0.0500    1.7528
##     20       14.4872             nan     0.0500    0.9270
##     40        5.7867             nan     0.0500    0.1248
##     60        3.8092             nan     0.0500    0.0091
##     80        3.2938             nan     0.0500   -0.0076
##    100        2.9802             nan     0.0500   -0.0080
##    120        2.7779             nan     0.0500   -0.0020
##    140        2.6243             nan     0.0500   -0.0292
##    160        2.4941             nan     0.0500   -0.0200
##    180        2.3758             nan     0.0500   -0.0170
##    200        2.2624             nan     0.0500   -0.0123
##    220        2.1762             nan     0.0500   -0.0141
##    240        2.0804             nan     0.0500   -0.0096
##    260        2.0210             nan     0.0500   -0.0121
##    280        1.9341             nan     0.0500   -0.0117
##    300        1.8632             nan     0.0500   -0.0080
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.4354             nan     0.0500    4.9324
##      2       52.6856             nan     0.0500    4.5665
##      3       48.5819             nan     0.0500    3.7521
##      4       44.7524             nan     0.0500    4.1088
##      5       41.1739             nan     0.0500    3.5443
##      6       38.1425             nan     0.0500    3.2636
##      7       35.2090             nan     0.0500    2.7641
##      8       32.4684             nan     0.0500    2.6513
##      9       29.8403             nan     0.0500    2.4102
##     10       27.6731             nan     0.0500    2.2611
##     20       14.3729             nan     0.0500    0.8711
##     40        6.1089             nan     0.0500    0.1803
##     60        4.1491             nan     0.0500    0.0240
##     80        3.5431             nan     0.0500   -0.0110
##    100        3.2463             nan     0.0500   -0.0157
##    120        3.0362             nan     0.0500   -0.0111
##    140        2.8519             nan     0.0500   -0.0163
##    160        2.7025             nan     0.0500   -0.0193
##    180        2.5846             nan     0.0500   -0.0037
##    200        2.4819             nan     0.0500   -0.0041
##    220        2.3853             nan     0.0500   -0.0085
##    240        2.2949             nan     0.0500   -0.0092
##    260        2.2406             nan     0.0500   -0.0161
##    280        2.1830             nan     0.0500   -0.0093
##    300        2.1130             nan     0.0500   -0.0128
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.7797             nan     0.0500    5.3080
##      2       51.7221             nan     0.0500    4.7184
##      3       47.3592             nan     0.0500    4.2967
##      4       43.3355             nan     0.0500    3.5109
##      5       39.6401             nan     0.0500    3.7149
##      6       36.5652             nan     0.0500    3.2352
##      7       33.4771             nan     0.0500    2.6516
##      8       30.9219             nan     0.0500    2.1424
##      9       28.2716             nan     0.0500    2.6244
##     10       26.0370             nan     0.0500    2.4572
##     20       12.4302             nan     0.0500    0.7796
##     40        4.5469             nan     0.0500    0.1346
##     60        2.9627             nan     0.0500   -0.0285
##     80        2.4488             nan     0.0500   -0.0064
##    100        2.1305             nan     0.0500    0.0015
##    120        1.9266             nan     0.0500   -0.0264
##    140        1.7322             nan     0.0500   -0.0261
##    160        1.5931             nan     0.0500   -0.0223
##    180        1.4557             nan     0.0500   -0.0110
##    200        1.3342             nan     0.0500   -0.0044
##    220        1.2308             nan     0.0500   -0.0125
##    240        1.1412             nan     0.0500   -0.0086
##    260        1.0518             nan     0.0500   -0.0058
##    280        0.9758             nan     0.0500   -0.0045
##    300        0.9112             nan     0.0500   -0.0121
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.8052             nan     0.0500    5.1934
##      2       51.9576             nan     0.0500    4.7822
##      3       47.6191             nan     0.0500    4.2905
##      4       43.7178             nan     0.0500    4.0496
##      5       39.9553             nan     0.0500    3.4817
##      6       36.8328             nan     0.0500    3.2097
##      7       33.8109             nan     0.0500    3.1067
##      8       31.0541             nan     0.0500    2.6924
##      9       28.7235             nan     0.0500    2.2073
##     10       26.4528             nan     0.0500    2.2677
##     20       12.5358             nan     0.0500    0.8226
##     40        4.6955             nan     0.0500    0.1092
##     60        3.1561             nan     0.0500    0.0207
##     80        2.6748             nan     0.0500   -0.0184
##    100        2.4147             nan     0.0500   -0.0238
##    120        2.2434             nan     0.0500   -0.0111
##    140        2.0750             nan     0.0500   -0.0276
##    160        1.9281             nan     0.0500   -0.0150
##    180        1.7972             nan     0.0500   -0.0111
##    200        1.6919             nan     0.0500   -0.0111
##    220        1.5931             nan     0.0500   -0.0073
##    240        1.5182             nan     0.0500   -0.0117
##    260        1.4274             nan     0.0500   -0.0136
##    280        1.3609             nan     0.0500   -0.0040
##    300        1.2874             nan     0.0500   -0.0084
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.8488             nan     0.0500    5.2633
##      2       52.1349             nan     0.0500    5.0083
##      3       47.7429             nan     0.0500    4.3664
##      4       43.6825             nan     0.0500    3.5465
##      5       40.0605             nan     0.0500    3.6206
##      6       36.7007             nan     0.0500    2.8452
##      7       33.6879             nan     0.0500    2.6694
##      8       30.9395             nan     0.0500    2.5053
##      9       28.5009             nan     0.0500    2.2607
##     10       26.3635             nan     0.0500    1.9661
##     20       12.9831             nan     0.0500    0.9210
##     40        5.0551             nan     0.0500    0.1290
##     60        3.5914             nan     0.0500    0.0214
##     80        3.0735             nan     0.0500   -0.0181
##    100        2.8128             nan     0.0500   -0.0209
##    120        2.5975             nan     0.0500   -0.0056
##    140        2.4276             nan     0.0500   -0.0124
##    160        2.2774             nan     0.0500   -0.0072
##    180        2.1701             nan     0.0500   -0.0080
##    200        2.0776             nan     0.0500   -0.0096
##    220        1.9822             nan     0.0500   -0.0170
##    240        1.8783             nan     0.0500   -0.0061
##    260        1.8112             nan     0.0500   -0.0115
##    280        1.7343             nan     0.0500   -0.0112
##    300        1.6591             nan     0.0500   -0.0067
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.8108             nan     0.1000    7.5007
##      2       48.0209             nan     0.1000    6.3378
##      3       43.1623             nan     0.1000    5.0967
##      4       38.6243             nan     0.1000    3.6919
##      5       34.6969             nan     0.1000    3.4864
##      6       31.6103             nan     0.1000    2.6766
##      7       28.7867             nan     0.1000    2.7828
##      8       26.4870             nan     0.1000    1.9932
##      9       23.9992             nan     0.1000    2.3550
##     10       22.0016             nan     0.1000    1.8610
##     20       11.0804             nan     0.1000    0.5230
##     40        5.3736             nan     0.1000    0.0995
##     60        4.1375             nan     0.1000   -0.0190
##     80        3.7725             nan     0.1000   -0.0368
##    100        3.5996             nan     0.1000   -0.0025
##    120        3.4697             nan     0.1000   -0.0097
##    140        3.3533             nan     0.1000   -0.0339
##    160        3.2347             nan     0.1000   -0.0025
##    180        3.1362             nan     0.1000   -0.0062
##    200        3.0574             nan     0.1000   -0.0078
##    220        2.9869             nan     0.1000   -0.0235
##    240        2.9183             nan     0.1000   -0.0226
##    260        2.8669             nan     0.1000   -0.0151
##    280        2.8035             nan     0.1000   -0.0129
##    300        2.7439             nan     0.1000   -0.0238
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.3443             nan     0.1000    6.8967
##      2       48.0079             nan     0.1000    5.9306
##      3       43.0792             nan     0.1000    4.6852
##      4       38.6414             nan     0.1000    4.3885
##      5       34.9396             nan     0.1000    3.2151
##      6       31.6789             nan     0.1000    2.4089
##      7       28.5329             nan     0.1000    3.0761
##      8       26.0846             nan     0.1000    2.2409
##      9       23.9617             nan     0.1000    2.0799
##     10       22.2316             nan     0.1000    1.8288
##     20       11.5716             nan     0.1000    0.3759
##     40        5.4864             nan     0.1000    0.0303
##     60        4.2544             nan     0.1000    0.0024
##     80        3.9044             nan     0.1000   -0.0193
##    100        3.7197             nan     0.1000   -0.0127
##    120        3.5601             nan     0.1000   -0.0130
##    140        3.4519             nan     0.1000   -0.0144
##    160        3.3760             nan     0.1000   -0.0096
##    180        3.3122             nan     0.1000   -0.0228
##    200        3.2220             nan     0.1000   -0.0170
##    220        3.1553             nan     0.1000   -0.0140
##    240        3.1171             nan     0.1000   -0.0422
##    260        3.0352             nan     0.1000   -0.0182
##    280        2.9947             nan     0.1000   -0.0119
##    300        2.9614             nan     0.1000   -0.0023
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.7861             nan     0.1000    7.5744
##      2       49.2234             nan     0.1000    5.6367
##      3       44.1796             nan     0.1000    4.2563
##      4       38.9259             nan     0.1000    5.0769
##      5       34.7983             nan     0.1000    3.8742
##      6       31.5173             nan     0.1000    3.5578
##      7       28.8699             nan     0.1000    2.6807
##      8       26.3956             nan     0.1000    2.2813
##      9       23.9863             nan     0.1000    1.9707
##     10       22.0448             nan     0.1000    1.8542
##     20       11.3213             nan     0.1000    0.5519
##     40        5.7017             nan     0.1000    0.0397
##     60        4.5587             nan     0.1000    0.0324
##     80        4.1787             nan     0.1000   -0.0074
##    100        3.9631             nan     0.1000   -0.0009
##    120        3.8200             nan     0.1000   -0.0306
##    140        3.7215             nan     0.1000   -0.0309
##    160        3.6270             nan     0.1000   -0.0148
##    180        3.5313             nan     0.1000   -0.0176
##    200        3.4447             nan     0.1000   -0.0205
##    220        3.3815             nan     0.1000   -0.0170
##    240        3.3005             nan     0.1000   -0.0038
##    260        3.2531             nan     0.1000   -0.0163
##    280        3.1755             nan     0.1000   -0.0069
##    300        3.1208             nan     0.1000   -0.0065
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.3223             nan     0.1000    8.8464
##      2       44.6657             nan     0.1000    7.3540
##      3       38.1758             nan     0.1000    6.7436
##      4       32.4947             nan     0.1000    5.6730
##      5       27.7318             nan     0.1000    4.9662
##      6       23.9998             nan     0.1000    3.4460
##      7       20.8132             nan     0.1000    3.0447
##      8       18.1872             nan     0.1000    2.6695
##      9       15.9176             nan     0.1000    2.1656
##     10       14.1803             nan     0.1000    1.7077
##     20        5.8587             nan     0.1000    0.2893
##     40        3.0812             nan     0.1000   -0.0118
##     60        2.5710             nan     0.1000   -0.0556
##     80        2.2303             nan     0.1000   -0.0287
##    100        1.9870             nan     0.1000   -0.0142
##    120        1.8109             nan     0.1000   -0.0182
##    140        1.6438             nan     0.1000   -0.0106
##    160        1.5217             nan     0.1000   -0.0321
##    180        1.4113             nan     0.1000   -0.0318
##    200        1.3098             nan     0.1000   -0.0207
##    220        1.2040             nan     0.1000   -0.0293
##    240        1.1049             nan     0.1000   -0.0143
##    260        1.0161             nan     0.1000   -0.0049
##    280        0.9439             nan     0.1000   -0.0135
##    300        0.8844             nan     0.1000   -0.0166
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.4021             nan     0.1000    9.0083
##      2       44.2496             nan     0.1000    8.2822
##      3       37.3361             nan     0.1000    5.9655
##      4       31.9045             nan     0.1000    5.0172
##      5       27.4969             nan     0.1000    4.2389
##      6       23.5336             nan     0.1000    3.3599
##      7       20.5659             nan     0.1000    2.9698
##      8       17.9630             nan     0.1000    2.7153
##      9       15.7517             nan     0.1000    2.0936
##     10       14.0820             nan     0.1000    1.5736
##     20        5.7109             nan     0.1000    0.2164
##     40        3.4308             nan     0.1000   -0.0207
##     60        2.8753             nan     0.1000   -0.0364
##     80        2.4971             nan     0.1000   -0.0164
##    100        2.2875             nan     0.1000   -0.0254
##    120        2.1093             nan     0.1000   -0.0271
##    140        1.9871             nan     0.1000   -0.0355
##    160        1.8746             nan     0.1000   -0.0181
##    180        1.7703             nan     0.1000   -0.0229
##    200        1.6605             nan     0.1000   -0.0157
##    220        1.5468             nan     0.1000   -0.0037
##    240        1.4602             nan     0.1000   -0.0220
##    260        1.3786             nan     0.1000   -0.0156
##    280        1.3131             nan     0.1000   -0.0282
##    300        1.2479             nan     0.1000   -0.0094
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.5432             nan     0.1000    8.9100
##      2       44.1516             nan     0.1000    7.9868
##      3       37.7469             nan     0.1000    6.9758
##      4       32.0433             nan     0.1000    5.1038
##      5       27.6694             nan     0.1000    4.1162
##      6       23.9474             nan     0.1000    3.7969
##      7       20.6435             nan     0.1000    2.9436
##      8       17.7614             nan     0.1000    2.3296
##      9       15.7301             nan     0.1000    2.2101
##     10       13.9316             nan     0.1000    1.6741
##     20        5.9425             nan     0.1000    0.2400
##     40        3.7335             nan     0.1000   -0.0056
##     60        3.1698             nan     0.1000   -0.0241
##     80        2.9054             nan     0.1000   -0.0358
##    100        2.6041             nan     0.1000   -0.0406
##    120        2.4028             nan     0.1000   -0.0254
##    140        2.2637             nan     0.1000   -0.0225
##    160        2.1652             nan     0.1000   -0.0313
##    180        2.0107             nan     0.1000   -0.0104
##    200        1.9042             nan     0.1000   -0.0235
##    220        1.8182             nan     0.1000   -0.0268
##    240        1.7075             nan     0.1000   -0.0145
##    260        1.6253             nan     0.1000   -0.0193
##    280        1.5627             nan     0.1000   -0.0164
##    300        1.4935             nan     0.1000   -0.0174
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.9336             nan     0.1000   10.3367
##      2       43.6281             nan     0.1000    8.0883
##      3       36.3628             nan     0.1000    7.6088
##      4       30.5856             nan     0.1000    5.5530
##      5       25.8329             nan     0.1000    4.8506
##      6       21.9819             nan     0.1000    3.9893
##      7       18.6486             nan     0.1000    2.6236
##      8       15.9182             nan     0.1000    2.6920
##      9       13.5781             nan     0.1000    2.1532
##     10       11.8844             nan     0.1000    1.5916
##     20        4.5107             nan     0.1000    0.3219
##     40        2.5758             nan     0.1000   -0.0362
##     60        2.0615             nan     0.1000   -0.0524
##     80        1.6985             nan     0.1000   -0.0403
##    100        1.4105             nan     0.1000   -0.0276
##    120        1.2082             nan     0.1000   -0.0308
##    140        1.0267             nan     0.1000   -0.0264
##    160        0.8917             nan     0.1000   -0.0163
##    180        0.7778             nan     0.1000   -0.0115
##    200        0.6890             nan     0.1000   -0.0143
##    220        0.6099             nan     0.1000   -0.0086
##    240        0.5326             nan     0.1000   -0.0082
##    260        0.4704             nan     0.1000   -0.0084
##    280        0.4184             nan     0.1000   -0.0099
##    300        0.3751             nan     0.1000   -0.0041
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.9032             nan     0.1000   11.0510
##      2       43.4474             nan     0.1000    6.7246
##      3       36.5066             nan     0.1000    6.7035
##      4       30.9613             nan     0.1000    5.5813
##      5       26.3727             nan     0.1000    4.8901
##      6       22.2448             nan     0.1000    3.7377
##      7       19.1221             nan     0.1000    2.8910
##      8       16.6911             nan     0.1000    2.3823
##      9       14.4348             nan     0.1000    1.9521
##     10       12.4688             nan     0.1000    1.6485
##     20        4.6909             nan     0.1000    0.2618
##     40        2.7556             nan     0.1000   -0.0361
##     60        2.2479             nan     0.1000   -0.0555
##     80        1.9144             nan     0.1000   -0.0355
##    100        1.6718             nan     0.1000   -0.0154
##    120        1.4829             nan     0.1000   -0.0251
##    140        1.3412             nan     0.1000   -0.0173
##    160        1.2034             nan     0.1000   -0.0212
##    180        1.1210             nan     0.1000   -0.0208
##    200        1.0161             nan     0.1000   -0.0135
##    220        0.9270             nan     0.1000   -0.0145
##    240        0.8585             nan     0.1000   -0.0190
##    260        0.8004             nan     0.1000   -0.0126
##    280        0.7373             nan     0.1000   -0.0092
##    300        0.6856             nan     0.1000   -0.0155
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.7761             nan     0.1000    9.3610
##      2       42.7726             nan     0.1000    8.9823
##      3       35.9887             nan     0.1000    6.6535
##      4       30.5143             nan     0.1000    5.0268
##      5       25.9943             nan     0.1000    3.8989
##      6       22.1294             nan     0.1000    3.6441
##      7       18.8914             nan     0.1000    3.0980
##      8       16.2929             nan     0.1000    2.3696
##      9       14.1484             nan     0.1000    2.1980
##     10       12.4185             nan     0.1000    1.6913
##     20        4.7602             nan     0.1000    0.1792
##     40        2.9744             nan     0.1000   -0.0121
##     60        2.5793             nan     0.1000   -0.0458
##     80        2.2736             nan     0.1000   -0.0357
##    100        2.0470             nan     0.1000   -0.0203
##    120        1.8663             nan     0.1000   -0.0388
##    140        1.7058             nan     0.1000   -0.0197
##    160        1.5690             nan     0.1000   -0.0272
##    180        1.4434             nan     0.1000   -0.0092
##    200        1.3260             nan     0.1000   -0.0100
##    220        1.2618             nan     0.1000   -0.0237
##    240        1.1755             nan     0.1000   -0.0068
##    260        1.0905             nan     0.1000   -0.0111
##    280        1.0337             nan     0.1000   -0.0177
##    300        0.9815             nan     0.1000   -0.0159
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.9985             nan     0.0100    0.7875
##      2       62.2174             nan     0.0100    0.8430
##      3       61.4593             nan     0.0100    0.7321
##      4       60.6916             nan     0.0100    0.7277
##      5       59.9171             nan     0.0100    0.6648
##      6       59.1093             nan     0.0100    0.7167
##      7       58.3689             nan     0.0100    0.7277
##      8       57.6622             nan     0.0100    0.7277
##      9       57.0034             nan     0.0100    0.6817
##     10       56.3371             nan     0.0100    0.6682
##     20       50.0316             nan     0.0100    0.5674
##     40       40.3489             nan     0.0100    0.4168
##     60       32.9237             nan     0.0100    0.2933
##     80       27.3044             nan     0.0100    0.2460
##    100       22.9947             nan     0.0100    0.1983
##    120       19.5744             nan     0.0100    0.1364
##    140       16.9284             nan     0.0100    0.0978
##    160       14.8381             nan     0.0100    0.0549
##    180       13.1188             nan     0.0100    0.0543
##    200       11.7126             nan     0.0100    0.0510
##    220       10.5697             nan     0.0100    0.0421
##    240        9.6024             nan     0.0100    0.0335
##    260        8.7650             nan     0.0100    0.0252
##    280        8.0587             nan     0.0100    0.0262
##    300        7.4808             nan     0.0100    0.0129
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.9728             nan     0.0100    0.8075
##      2       62.2199             nan     0.0100    0.6267
##      3       61.3663             nan     0.0100    0.8007
##      4       60.5435             nan     0.0100    0.7518
##      5       59.7788             nan     0.0100    0.7242
##      6       59.0246             nan     0.0100    0.7037
##      7       58.2770             nan     0.0100    0.6863
##      8       57.6106             nan     0.0100    0.6865
##      9       56.8931             nan     0.0100    0.7189
##     10       56.1347             nan     0.0100    0.6958
##     20       49.9446             nan     0.0100    0.5677
##     40       40.1068             nan     0.0100    0.3826
##     60       32.7367             nan     0.0100    0.3046
##     80       27.2530             nan     0.0100    0.1204
##    100       22.9359             nan     0.0100    0.1727
##    120       19.6452             nan     0.0100    0.1264
##    140       17.0291             nan     0.0100    0.0933
##    160       14.8957             nan     0.0100    0.0621
##    180       13.1704             nan     0.0100    0.0646
##    200       11.7834             nan     0.0100    0.0420
##    220       10.6054             nan     0.0100    0.0453
##    240        9.6261             nan     0.0100    0.0382
##    260        8.7814             nan     0.0100    0.0347
##    280        8.0555             nan     0.0100    0.0203
##    300        7.4630             nan     0.0100    0.0138
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       63.0177             nan     0.0100    0.7986
##      2       62.2306             nan     0.0100    0.7906
##      3       61.4139             nan     0.0100    0.7346
##      4       60.6331             nan     0.0100    0.7934
##      5       59.9006             nan     0.0100    0.7553
##      6       59.1494             nan     0.0100    0.7061
##      7       58.4641             nan     0.0100    0.7200
##      8       57.7236             nan     0.0100    0.6793
##      9       56.9957             nan     0.0100    0.6509
##     10       56.3227             nan     0.0100    0.6699
##     20       50.0636             nan     0.0100    0.4560
##     40       40.2629             nan     0.0100    0.3811
##     60       32.8189             nan     0.0100    0.2948
##     80       27.2182             nan     0.0100    0.2229
##    100       23.0342             nan     0.0100    0.1894
##    120       19.6412             nan     0.0100    0.1346
##    140       17.0362             nan     0.0100    0.1132
##    160       14.9164             nan     0.0100    0.0774
##    180       13.1875             nan     0.0100    0.0667
##    200       11.7668             nan     0.0100    0.0506
##    220       10.6012             nan     0.0100    0.0413
##    240        9.6379             nan     0.0100    0.0366
##    260        8.8481             nan     0.0100    0.0326
##    280        8.1773             nan     0.0100    0.0273
##    300        7.5830             nan     0.0100    0.0212
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.8472             nan     0.0100    1.0580
##      2       61.8151             nan     0.0100    1.0282
##      3       60.7652             nan     0.0100    0.9007
##      4       59.7377             nan     0.0100    0.9919
##      5       58.7778             nan     0.0100    0.9584
##      6       57.8416             nan     0.0100    0.9681
##      7       56.8664             nan     0.0100    0.9437
##      8       55.9463             nan     0.0100    1.0023
##      9       54.9986             nan     0.0100    0.8953
##     10       54.1560             nan     0.0100    0.8761
##     20       46.2454             nan     0.0100    0.8056
##     40       33.9783             nan     0.0100    0.5523
##     60       25.3631             nan     0.0100    0.2939
##     80       19.3733             nan     0.0100    0.2572
##    100       14.9972             nan     0.0100    0.1952
##    120       11.9489             nan     0.0100    0.1146
##    140        9.7948             nan     0.0100    0.0841
##    160        8.1664             nan     0.0100    0.0520
##    180        6.9351             nan     0.0100    0.0401
##    200        6.0076             nan     0.0100    0.0287
##    220        5.3065             nan     0.0100    0.0260
##    240        4.7662             nan     0.0100    0.0192
##    260        4.3874             nan     0.0100    0.0037
##    280        4.0867             nan     0.0100    0.0093
##    300        3.8419             nan     0.0100    0.0078
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.7955             nan     0.0100    1.1215
##      2       61.7435             nan     0.0100    0.9638
##      3       60.6800             nan     0.0100    1.0060
##      4       59.6701             nan     0.0100    0.9358
##      5       58.6875             nan     0.0100    0.9502
##      6       57.7191             nan     0.0100    0.9013
##      7       56.7564             nan     0.0100    0.8782
##      8       55.8102             nan     0.0100    1.0219
##      9       54.8753             nan     0.0100    0.7793
##     10       53.9803             nan     0.0100    0.7803
##     20       45.9157             nan     0.0100    0.7360
##     40       33.6841             nan     0.0100    0.4659
##     60       25.0701             nan     0.0100    0.3866
##     80       19.0979             nan     0.0100    0.2252
##    100       14.8930             nan     0.0100    0.1779
##    120       11.8803             nan     0.0100    0.1228
##    140        9.7416             nan     0.0100    0.0843
##    160        8.1806             nan     0.0100    0.0573
##    180        7.0130             nan     0.0100    0.0458
##    200        6.0820             nan     0.0100    0.0307
##    220        5.4204             nan     0.0100    0.0116
##    240        4.8834             nan     0.0100    0.0197
##    260        4.4943             nan     0.0100    0.0112
##    280        4.2012             nan     0.0100    0.0040
##    300        3.9609             nan     0.0100    0.0024
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.7610             nan     0.0100    1.1142
##      2       61.7814             nan     0.0100    1.0401
##      3       60.7940             nan     0.0100    0.9031
##      4       59.7810             nan     0.0100    0.9792
##      5       58.8219             nan     0.0100    0.9726
##      6       57.8467             nan     0.0100    1.0026
##      7       56.8668             nan     0.0100    0.8839
##      8       55.8837             nan     0.0100    0.8685
##      9       54.9786             nan     0.0100    0.9190
##     10       54.1008             nan     0.0100    0.9809
##     20       46.0141             nan     0.0100    0.7153
##     40       33.8735             nan     0.0100    0.4247
##     60       25.3138             nan     0.0100    0.3529
##     80       19.3380             nan     0.0100    0.2140
##    100       15.1335             nan     0.0100    0.1771
##    120       12.0413             nan     0.0100    0.1170
##    140        9.8881             nan     0.0100    0.0918
##    160        8.2730             nan     0.0100    0.0556
##    180        7.1074             nan     0.0100    0.0431
##    200        6.2181             nan     0.0100    0.0334
##    220        5.4841             nan     0.0100    0.0248
##    240        4.9866             nan     0.0100    0.0164
##    260        4.6185             nan     0.0100    0.0092
##    280        4.3113             nan     0.0100    0.0071
##    300        4.0936             nan     0.0100    0.0082
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.7480             nan     0.0100    0.9854
##      2       61.6227             nan     0.0100    1.1596
##      3       60.5054             nan     0.0100    1.2123
##      4       59.4158             nan     0.0100    0.9801
##      5       58.3669             nan     0.0100    0.9509
##      6       57.3524             nan     0.0100    1.0987
##      7       56.3448             nan     0.0100    1.1202
##      8       55.3708             nan     0.0100    0.7840
##      9       54.4041             nan     0.0100    0.9042
##     10       53.4696             nan     0.0100    0.8723
##     20       45.1079             nan     0.0100    0.7032
##     40       32.2431             nan     0.0100    0.5311
##     60       23.4316             nan     0.0100    0.3352
##     80       17.3572             nan     0.0100    0.2649
##    100       13.0953             nan     0.0100    0.1503
##    120       10.1238             nan     0.0100    0.1069
##    140        8.0787             nan     0.0100    0.0690
##    160        6.5862             nan     0.0100    0.0594
##    180        5.5409             nan     0.0100    0.0293
##    200        4.7941             nan     0.0100    0.0234
##    220        4.2283             nan     0.0100    0.0126
##    240        3.7993             nan     0.0100    0.0093
##    260        3.4848             nan     0.0100    0.0033
##    280        3.2269             nan     0.0100    0.0041
##    300        3.0365             nan     0.0100   -0.0025
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.7756             nan     0.0100    1.1535
##      2       61.6614             nan     0.0100    1.1594
##      3       60.5751             nan     0.0100    1.0897
##      4       59.4898             nan     0.0100    1.1126
##      5       58.4567             nan     0.0100    1.0362
##      6       57.4327             nan     0.0100    0.9636
##      7       56.3995             nan     0.0100    0.9951
##      8       55.3840             nan     0.0100    0.9755
##      9       54.4486             nan     0.0100    0.9077
##     10       53.5274             nan     0.0100    0.8308
##     20       45.1321             nan     0.0100    0.7084
##     40       32.4246             nan     0.0100    0.4667
##     60       23.5740             nan     0.0100    0.3287
##     80       17.5295             nan     0.0100    0.2455
##    100       13.2449             nan     0.0100    0.1571
##    120       10.3244             nan     0.0100    0.1158
##    140        8.2139             nan     0.0100    0.0646
##    160        6.7541             nan     0.0100    0.0378
##    180        5.7101             nan     0.0100    0.0431
##    200        4.9589             nan     0.0100    0.0234
##    220        4.4198             nan     0.0100    0.0154
##    240        4.0180             nan     0.0100    0.0119
##    260        3.7349             nan     0.0100    0.0071
##    280        3.4961             nan     0.0100    0.0057
##    300        3.3209             nan     0.0100   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       62.7386             nan     0.0100    1.1608
##      2       61.6411             nan     0.0100    1.0759
##      3       60.5095             nan     0.0100    1.0758
##      4       59.4845             nan     0.0100    0.9343
##      5       58.5064             nan     0.0100    1.0502
##      6       57.4671             nan     0.0100    0.9650
##      7       56.4945             nan     0.0100    1.0243
##      8       55.5046             nan     0.0100    0.9557
##      9       54.4976             nan     0.0100    0.8931
##     10       53.5467             nan     0.0100    0.7987
##     20       45.1999             nan     0.0100    0.7831
##     40       32.5786             nan     0.0100    0.5120
##     60       23.7932             nan     0.0100    0.3348
##     80       17.7073             nan     0.0100    0.2131
##    100       13.4632             nan     0.0100    0.1663
##    120       10.5338             nan     0.0100    0.1182
##    140        8.4930             nan     0.0100    0.0772
##    160        7.0194             nan     0.0100    0.0454
##    180        5.9687             nan     0.0100    0.0327
##    200        5.2346             nan     0.0100    0.0320
##    220        4.6896             nan     0.0100    0.0176
##    240        4.2815             nan     0.0100    0.0091
##    260        3.9894             nan     0.0100    0.0070
##    280        3.7589             nan     0.0100   -0.0015
##    300        3.5781             nan     0.0100    0.0057
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.9098             nan     0.0500    4.1048
##      2       56.4271             nan     0.0500    3.6368
##      3       53.0966             nan     0.0500    3.2035
##      4       50.1659             nan     0.0500    3.1309
##      5       47.5503             nan     0.0500    2.6222
##      6       44.7888             nan     0.0500    2.5692
##      7       42.6590             nan     0.0500    2.1013
##      8       40.5665             nan     0.0500    1.9516
##      9       38.4932             nan     0.0500    2.1868
##     10       36.4560             nan     0.0500    1.8213
##     20       23.4781             nan     0.0500    0.9012
##     40       11.8874             nan     0.0500    0.2211
##     60        7.5922             nan     0.0500    0.0636
##     80        5.7229             nan     0.0500    0.0263
##    100        4.7217             nan     0.0500    0.0176
##    120        4.2670             nan     0.0500   -0.0032
##    140        4.0540             nan     0.0500   -0.0170
##    160        3.9030             nan     0.0500   -0.0012
##    180        3.8103             nan     0.0500   -0.0150
##    200        3.7078             nan     0.0500   -0.0027
##    220        3.6273             nan     0.0500   -0.0044
##    240        3.5434             nan     0.0500   -0.0043
##    260        3.4911             nan     0.0500   -0.0070
##    280        3.4394             nan     0.0500   -0.0119
##    300        3.3906             nan     0.0500   -0.0059
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       59.6441             nan     0.0500    3.7437
##      2       56.0616             nan     0.0500    3.3833
##      3       52.4354             nan     0.0500    3.4142
##      4       49.6572             nan     0.0500    2.7917
##      5       46.9582             nan     0.0500    2.5597
##      6       44.6289             nan     0.0500    2.5130
##      7       42.1087             nan     0.0500    2.4419
##      8       39.9137             nan     0.0500    2.1136
##      9       37.8415             nan     0.0500    2.1188
##     10       35.9150             nan     0.0500    1.7569
##     20       22.4811             nan     0.0500    0.8767
##     40       11.5685             nan     0.0500    0.2598
##     60        7.3333             nan     0.0500    0.1086
##     80        5.4810             nan     0.0500    0.0296
##    100        4.6179             nan     0.0500    0.0190
##    120        4.1811             nan     0.0500    0.0026
##    140        3.9598             nan     0.0500    0.0009
##    160        3.8300             nan     0.0500   -0.0134
##    180        3.7330             nan     0.0500   -0.0015
##    200        3.6539             nan     0.0500   -0.0107
##    220        3.5875             nan     0.0500   -0.0024
##    240        3.5257             nan     0.0500   -0.0077
##    260        3.4681             nan     0.0500   -0.0058
##    280        3.4136             nan     0.0500   -0.0014
##    300        3.3698             nan     0.0500   -0.0084
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.0469             nan     0.0500    4.2440
##      2       56.7843             nan     0.0500    3.6193
##      3       53.5268             nan     0.0500    3.5454
##      4       50.3479             nan     0.0500    2.9587
##      5       47.2906             nan     0.0500    2.7413
##      6       44.6741             nan     0.0500    2.4363
##      7       42.3988             nan     0.0500    2.1610
##      8       40.1805             nan     0.0500    2.2103
##      9       38.0956             nan     0.0500    1.8684
##     10       36.1982             nan     0.0500    1.9085
##     20       22.8428             nan     0.0500    0.9003
##     40       11.7585             nan     0.0500    0.2078
##     60        7.5963             nan     0.0500    0.1208
##     80        5.6870             nan     0.0500    0.0073
##    100        4.8346             nan     0.0500    0.0062
##    120        4.4647             nan     0.0500    0.0001
##    140        4.2605             nan     0.0500   -0.0072
##    160        4.1150             nan     0.0500    0.0017
##    180        4.0121             nan     0.0500   -0.0009
##    200        3.9124             nan     0.0500   -0.0246
##    220        3.8312             nan     0.0500    0.0003
##    240        3.7587             nan     0.0500   -0.0031
##    260        3.6907             nan     0.0500   -0.0183
##    280        3.6319             nan     0.0500   -0.0143
##    300        3.5749             nan     0.0500   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.7636             nan     0.0500    5.1433
##      2       54.0719             nan     0.0500    4.1461
##      3       49.7294             nan     0.0500    4.0655
##      4       45.8498             nan     0.0500    4.0425
##      5       42.2136             nan     0.0500    3.4826
##      6       38.9837             nan     0.0500    3.1701
##      7       36.1836             nan     0.0500    2.4554
##      8       33.5145             nan     0.0500    2.7270
##      9       31.0261             nan     0.0500    2.6203
##     10       28.9104             nan     0.0500    2.2185
##     20       14.5809             nan     0.0500    0.9342
##     40        5.8341             nan     0.0500    0.1041
##     60        3.7838             nan     0.0500    0.0263
##     80        3.1607             nan     0.0500    0.0126
##    100        2.8608             nan     0.0500   -0.0172
##    120        2.6389             nan     0.0500   -0.0127
##    140        2.4352             nan     0.0500   -0.0089
##    160        2.2785             nan     0.0500   -0.0009
##    180        2.1598             nan     0.0500   -0.0006
##    200        2.0275             nan     0.0500   -0.0229
##    220        1.9335             nan     0.0500   -0.0097
##    240        1.8510             nan     0.0500   -0.0099
##    260        1.7582             nan     0.0500   -0.0070
##    280        1.6813             nan     0.0500   -0.0074
##    300        1.6014             nan     0.0500   -0.0134
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.5867             nan     0.0500    5.2170
##      2       54.0468             nan     0.0500    4.2882
##      3       49.8288             nan     0.0500    4.4252
##      4       45.9101             nan     0.0500    3.9567
##      5       42.3846             nan     0.0500    3.6024
##      6       39.3939             nan     0.0500    3.6098
##      7       36.4173             nan     0.0500    3.0475
##      8       33.7597             nan     0.0500    2.5965
##      9       31.1969             nan     0.0500    2.4442
##     10       28.8064             nan     0.0500    2.2393
##     20       14.7728             nan     0.0500    0.7685
##     40        5.9743             nan     0.0500    0.1615
##     60        4.0018             nan     0.0500    0.0378
##     80        3.3948             nan     0.0500    0.0056
##    100        3.0703             nan     0.0500   -0.0164
##    120        2.8332             nan     0.0500   -0.0131
##    140        2.6644             nan     0.0500   -0.0022
##    160        2.5310             nan     0.0500   -0.0085
##    180        2.3943             nan     0.0500   -0.0160
##    200        2.2851             nan     0.0500   -0.0026
##    220        2.1822             nan     0.0500   -0.0140
##    240        2.1083             nan     0.0500   -0.0077
##    260        2.0309             nan     0.0500   -0.0073
##    280        1.9559             nan     0.0500   -0.0088
##    300        1.8858             nan     0.0500   -0.0126
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.8172             nan     0.0500    5.0553
##      2       53.9460             nan     0.0500    4.9119
##      3       49.8320             nan     0.0500    4.3022
##      4       45.7921             nan     0.0500    4.1106
##      5       42.4456             nan     0.0500    3.3215
##      6       39.1334             nan     0.0500    2.8395
##      7       36.0692             nan     0.0500    3.2623
##      8       33.3416             nan     0.0500    2.5261
##      9       30.8669             nan     0.0500    2.3282
##     10       28.5887             nan     0.0500    2.2618
##     20       14.5897             nan     0.0500    0.8836
##     40        6.1577             nan     0.0500    0.1726
##     60        4.1437             nan     0.0500    0.0334
##     80        3.5131             nan     0.0500    0.0210
##    100        3.2283             nan     0.0500   -0.0141
##    120        2.9968             nan     0.0500   -0.0204
##    140        2.8371             nan     0.0500   -0.0056
##    160        2.6838             nan     0.0500   -0.0142
##    180        2.5663             nan     0.0500   -0.0203
##    200        2.4534             nan     0.0500   -0.0067
##    220        2.3746             nan     0.0500   -0.0091
##    240        2.2865             nan     0.0500   -0.0051
##    260        2.2246             nan     0.0500   -0.0157
##    280        2.1581             nan     0.0500   -0.0094
##    300        2.0965             nan     0.0500   -0.0149
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.3393             nan     0.0500    5.1045
##      2       53.2065             nan     0.0500    5.1345
##      3       48.5108             nan     0.0500    4.8570
##      4       44.3641             nan     0.0500    3.6656
##      5       40.9514             nan     0.0500    3.7283
##      6       37.5076             nan     0.0500    3.3830
##      7       34.4473             nan     0.0500    2.9459
##      8       31.6661             nan     0.0500    2.7402
##      9       29.1464             nan     0.0500    2.4814
##     10       26.8289             nan     0.0500    2.0719
##     20       12.9020             nan     0.0500    0.7695
##     40        4.8440             nan     0.0500    0.0998
##     60        3.0419             nan     0.0500    0.0075
##     80        2.5065             nan     0.0500   -0.0242
##    100        2.1707             nan     0.0500   -0.0107
##    120        1.9493             nan     0.0500   -0.0155
##    140        1.7679             nan     0.0500   -0.0155
##    160        1.5994             nan     0.0500   -0.0232
##    180        1.4622             nan     0.0500   -0.0177
##    200        1.3392             nan     0.0500   -0.0166
##    220        1.2215             nan     0.0500   -0.0102
##    240        1.1252             nan     0.0500   -0.0116
##    260        1.0387             nan     0.0500   -0.0144
##    280        0.9759             nan     0.0500   -0.0087
##    300        0.9170             nan     0.0500   -0.0085
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.0569             nan     0.0500    5.8005
##      2       53.2120             nan     0.0500    4.3338
##      3       48.5908             nan     0.0500    4.7339
##      4       44.5846             nan     0.0500    3.8315
##      5       41.0023             nan     0.0500    3.6521
##      6       37.7011             nan     0.0500    3.4177
##      7       34.5523             nan     0.0500    2.4721
##      8       31.7436             nan     0.0500    2.5796
##      9       29.2161             nan     0.0500    2.5167
##     10       27.0388             nan     0.0500    2.0536
##     20       13.1026             nan     0.0500    0.7898
##     40        4.7899             nan     0.0500    0.1368
##     60        3.1979             nan     0.0500   -0.0115
##     80        2.7368             nan     0.0500    0.0016
##    100        2.4480             nan     0.0500   -0.0069
##    120        2.2418             nan     0.0500   -0.0086
##    140        2.0646             nan     0.0500   -0.0132
##    160        1.9299             nan     0.0500   -0.0106
##    180        1.7936             nan     0.0500   -0.0151
##    200        1.6881             nan     0.0500   -0.0099
##    220        1.5813             nan     0.0500   -0.0165
##    240        1.4927             nan     0.0500   -0.0082
##    260        1.4125             nan     0.0500   -0.0106
##    280        1.3407             nan     0.0500   -0.0105
##    300        1.2748             nan     0.0500   -0.0063
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       58.5613             nan     0.0500    5.1229
##      2       53.7997             nan     0.0500    5.4120
##      3       49.4664             nan     0.0500    4.2559
##      4       45.2781             nan     0.0500    4.2120
##      5       41.3838             nan     0.0500    3.5533
##      6       37.9544             nan     0.0500    3.2361
##      7       34.8002             nan     0.0500    2.7159
##      8       31.8574             nan     0.0500    2.7398
##      9       29.3410             nan     0.0500    2.5587
##     10       27.1484             nan     0.0500    2.0509
##     20       13.0973             nan     0.0500    0.8598
##     40        5.1876             nan     0.0500    0.1188
##     60        3.6646             nan     0.0500    0.0025
##     80        3.1571             nan     0.0500   -0.0155
##    100        2.8687             nan     0.0500   -0.0219
##    120        2.6294             nan     0.0500   -0.0223
##    140        2.4278             nan     0.0500   -0.0298
##    160        2.2907             nan     0.0500   -0.0109
##    180        2.1632             nan     0.0500   -0.0107
##    200        2.0605             nan     0.0500   -0.0187
##    220        1.9530             nan     0.0500   -0.0143
##    240        1.8823             nan     0.0500   -0.0125
##    260        1.8020             nan     0.0500   -0.0163
##    280        1.7263             nan     0.0500   -0.0136
##    300        1.6653             nan     0.0500   -0.0064
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.7107             nan     0.1000    7.1249
##      2       49.9424             nan     0.1000    6.8176
##      3       44.9423             nan     0.1000    5.4342
##      4       40.2198             nan     0.1000    4.5277
##      5       36.0712             nan     0.1000    3.3928
##      6       32.6024             nan     0.1000    3.4584
##      7       29.5447             nan     0.1000    2.7444
##      8       26.9974             nan     0.1000    2.4453
##      9       24.6215             nan     0.1000    2.3974
##     10       22.6000             nan     0.1000    1.5529
##     20       11.6752             nan     0.1000    0.4538
##     40        5.7940             nan     0.1000    0.0529
##     60        4.4432             nan     0.1000    0.0219
##     80        4.0681             nan     0.1000    0.0001
##    100        3.8773             nan     0.1000   -0.0022
##    120        3.6893             nan     0.1000   -0.0028
##    140        3.5515             nan     0.1000   -0.0605
##    160        3.4202             nan     0.1000   -0.0120
##    180        3.3227             nan     0.1000   -0.0218
##    200        3.2451             nan     0.1000   -0.0010
##    220        3.1666             nan     0.1000   -0.0259
##    240        3.0957             nan     0.1000   -0.0099
##    260        3.0286             nan     0.1000   -0.0066
##    280        2.9555             nan     0.1000   -0.0211
##    300        2.9066             nan     0.1000   -0.0321
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       55.7692             nan     0.1000    7.9460
##      2       49.4909             nan     0.1000    6.1097
##      3       44.2695             nan     0.1000    4.8559
##      4       40.0814             nan     0.1000    3.7968
##      5       36.3905             nan     0.1000    3.8122
##      6       32.8987             nan     0.1000    3.6521
##      7       29.7813             nan     0.1000    2.9491
##      8       27.0564             nan     0.1000    2.7733
##      9       24.4975             nan     0.1000    2.4207
##     10       22.4945             nan     0.1000    1.7237
##     20       11.7907             nan     0.1000    0.4792
##     40        5.7844             nan     0.1000    0.1214
##     60        4.3809             nan     0.1000    0.0073
##     80        4.0432             nan     0.1000   -0.0040
##    100        3.8185             nan     0.1000   -0.0221
##    120        3.6507             nan     0.1000   -0.0031
##    140        3.5547             nan     0.1000   -0.0223
##    160        3.4571             nan     0.1000   -0.0153
##    180        3.3754             nan     0.1000   -0.0173
##    200        3.3178             nan     0.1000   -0.0201
##    220        3.2456             nan     0.1000   -0.0263
##    240        3.1805             nan     0.1000   -0.0181
##    260        3.1171             nan     0.1000   -0.0124
##    280        3.0690             nan     0.1000   -0.0162
##    300        3.0136             nan     0.1000   -0.0227
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.6537             nan     0.1000    7.8991
##      2       50.3931             nan     0.1000    6.2872
##      3       45.0122             nan     0.1000    5.3608
##      4       40.9096             nan     0.1000    3.8877
##      5       36.7605             nan     0.1000    3.7550
##      6       33.2573             nan     0.1000    3.3243
##      7       29.9867             nan     0.1000    2.8839
##      8       27.3614             nan     0.1000    2.2408
##      9       24.9204             nan     0.1000    2.2853
##     10       23.1194             nan     0.1000    1.6966
##     20       11.9908             nan     0.1000    0.5310
##     40        5.8346             nan     0.1000    0.1196
##     60        4.6497             nan     0.1000   -0.0195
##     80        4.2677             nan     0.1000   -0.0409
##    100        4.0744             nan     0.1000   -0.0186
##    120        3.8959             nan     0.1000   -0.0138
##    140        3.7453             nan     0.1000   -0.0338
##    160        3.6214             nan     0.1000   -0.0071
##    180        3.5386             nan     0.1000   -0.0160
##    200        3.4588             nan     0.1000   -0.0171
##    220        3.4076             nan     0.1000   -0.0253
##    240        3.3565             nan     0.1000   -0.0274
##    260        3.2951             nan     0.1000   -0.0206
##    280        3.2426             nan     0.1000   -0.0497
##    300        3.1803             nan     0.1000   -0.0058
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.7873             nan     0.1000    9.1290
##      2       45.7962             nan     0.1000    7.7137
##      3       38.7359             nan     0.1000    7.1249
##      4       32.9892             nan     0.1000    5.7700
##      5       28.4315             nan     0.1000    4.3602
##      6       24.5868             nan     0.1000    3.5410
##      7       21.3789             nan     0.1000    3.1941
##      8       18.4510             nan     0.1000    2.6498
##      9       16.3676             nan     0.1000    2.1125
##     10       14.3417             nan     0.1000    1.7946
##     20        5.8491             nan     0.1000    0.3472
##     40        3.2467             nan     0.1000    0.0056
##     60        2.6828             nan     0.1000   -0.0118
##     80        2.2998             nan     0.1000   -0.0206
##    100        2.0345             nan     0.1000   -0.0233
##    120        1.8409             nan     0.1000   -0.0126
##    140        1.6791             nan     0.1000   -0.0163
##    160        1.5443             nan     0.1000   -0.0178
##    180        1.4157             nan     0.1000   -0.0160
##    200        1.3092             nan     0.1000   -0.0176
##    220        1.2289             nan     0.1000   -0.0090
##    240        1.1451             nan     0.1000   -0.0043
##    260        1.0615             nan     0.1000   -0.0227
##    280        0.9799             nan     0.1000   -0.0122
##    300        0.9200             nan     0.1000   -0.0091
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.7308             nan     0.1000   10.1487
##      2       45.2547             nan     0.1000    8.7644
##      3       38.0509             nan     0.1000    7.3119
##      4       32.2444             nan     0.1000    5.4438
##      5       27.5389             nan     0.1000    4.3646
##      6       23.6937             nan     0.1000    3.3059
##      7       20.4775             nan     0.1000    2.9580
##      8       18.0230             nan     0.1000    2.5625
##      9       15.7928             nan     0.1000    2.2526
##     10       13.9186             nan     0.1000    1.7400
##     20        6.0184             nan     0.1000    0.2870
##     40        3.4837             nan     0.1000   -0.0186
##     60        2.8838             nan     0.1000   -0.0242
##     80        2.5470             nan     0.1000   -0.0324
##    100        2.2953             nan     0.1000   -0.0206
##    120        2.1178             nan     0.1000   -0.0265
##    140        1.9513             nan     0.1000   -0.0276
##    160        1.8246             nan     0.1000   -0.0446
##    180        1.7260             nan     0.1000   -0.0257
##    200        1.6415             nan     0.1000   -0.0203
##    220        1.5431             nan     0.1000   -0.0176
##    240        1.4581             nan     0.1000   -0.0107
##    260        1.3837             nan     0.1000    0.0000
##    280        1.3080             nan     0.1000   -0.0184
##    300        1.2463             nan     0.1000   -0.0138
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.3594             nan     0.1000    9.9251
##      2       44.5474             nan     0.1000    8.3285
##      3       37.8864             nan     0.1000    7.0288
##      4       32.1471             nan     0.1000    5.0124
##      5       27.7939             nan     0.1000    4.6844
##      6       23.9038             nan     0.1000    3.7461
##      7       20.8659             nan     0.1000    3.1656
##      8       18.3206             nan     0.1000    2.2065
##      9       15.8902             nan     0.1000    1.7098
##     10       13.9527             nan     0.1000    1.4113
##     20        6.0328             nan     0.1000    0.3251
##     40        3.4588             nan     0.1000    0.0191
##     60        2.9379             nan     0.1000   -0.0088
##     80        2.6484             nan     0.1000   -0.0270
##    100        2.4853             nan     0.1000   -0.0013
##    120        2.3029             nan     0.1000   -0.0151
##    140        2.1725             nan     0.1000   -0.0332
##    160        2.0444             nan     0.1000   -0.0456
##    180        1.9345             nan     0.1000   -0.0239
##    200        1.8337             nan     0.1000   -0.0175
##    220        1.7398             nan     0.1000   -0.0212
##    240        1.6474             nan     0.1000   -0.0359
##    260        1.5777             nan     0.1000   -0.0213
##    280        1.5068             nan     0.1000   -0.0181
##    300        1.4430             nan     0.1000   -0.0170
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.0479             nan     0.1000    9.8548
##      2       44.5334             nan     0.1000    7.5746
##      3       36.9696             nan     0.1000    7.1489
##      4       31.1362             nan     0.1000    4.9150
##      5       26.3586             nan     0.1000    4.7039
##      6       22.3089             nan     0.1000    3.8827
##      7       19.0925             nan     0.1000    2.8275
##      8       16.4445             nan     0.1000    2.6879
##      9       14.2281             nan     0.1000    2.2221
##     10       12.5914             nan     0.1000    1.6502
##     20        4.7730             nan     0.1000    0.2575
##     40        2.6609             nan     0.1000   -0.0291
##     60        2.0643             nan     0.1000   -0.0378
##     80        1.7014             nan     0.1000   -0.0381
##    100        1.4711             nan     0.1000   -0.0197
##    120        1.2311             nan     0.1000   -0.0223
##    140        1.0535             nan     0.1000   -0.0255
##    160        0.9040             nan     0.1000   -0.0086
##    180        0.7878             nan     0.1000   -0.0129
##    200        0.6791             nan     0.1000   -0.0121
##    220        0.6108             nan     0.1000   -0.0088
##    240        0.5407             nan     0.1000   -0.0070
##    260        0.4872             nan     0.1000   -0.0087
##    280        0.4424             nan     0.1000   -0.0106
##    300        0.3954             nan     0.1000   -0.0063
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.3589             nan     0.1000    9.8892
##      2       44.4164             nan     0.1000    8.4572
##      3       37.4332             nan     0.1000    6.8001
##      4       31.4089             nan     0.1000    6.7308
##      5       26.4813             nan     0.1000    4.5053
##      6       22.7440             nan     0.1000    3.4369
##      7       19.6367             nan     0.1000    2.9010
##      8       16.8183             nan     0.1000    2.8924
##      9       14.5057             nan     0.1000    2.3272
##     10       12.5532             nan     0.1000    1.7684
##     20        4.8839             nan     0.1000    0.2116
##     40        2.9095             nan     0.1000   -0.0104
##     60        2.3911             nan     0.1000   -0.0290
##     80        2.0573             nan     0.1000   -0.0123
##    100        1.8176             nan     0.1000   -0.0357
##    120        1.6304             nan     0.1000   -0.0341
##    140        1.4759             nan     0.1000   -0.0242
##    160        1.3384             nan     0.1000   -0.0333
##    180        1.1946             nan     0.1000   -0.0193
##    200        1.0783             nan     0.1000   -0.0194
##    220        0.9832             nan     0.1000   -0.0214
##    240        0.9016             nan     0.1000   -0.0080
##    260        0.8464             nan     0.1000   -0.0216
##    280        0.7652             nan     0.1000   -0.0049
##    300        0.7142             nan     0.1000   -0.0139
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.8167             nan     0.1000   10.5142
##      2       44.0910             nan     0.1000    8.7777
##      3       36.7865             nan     0.1000    7.6310
##      4       30.8366             nan     0.1000    5.4251
##      5       26.1056             nan     0.1000    4.5183
##      6       22.1096             nan     0.1000    4.1077
##      7       18.8688             nan     0.1000    2.8525
##      8       16.2652             nan     0.1000    2.5781
##      9       14.1881             nan     0.1000    1.7926
##     10       12.6016             nan     0.1000    1.5399
##     20        5.1494             nan     0.1000    0.2840
##     40        3.1300             nan     0.1000    0.0286
##     60        2.6763             nan     0.1000   -0.0292
##     80        2.3678             nan     0.1000   -0.0172
##    100        2.1244             nan     0.1000   -0.0044
##    120        1.9211             nan     0.1000   -0.0059
##    140        1.7671             nan     0.1000   -0.0238
##    160        1.6278             nan     0.1000   -0.0343
##    180        1.5020             nan     0.1000   -0.0219
##    200        1.3941             nan     0.1000   -0.0026
##    220        1.3018             nan     0.1000   -0.0218
##    240        1.2041             nan     0.1000   -0.0102
##    260        1.1194             nan     0.1000   -0.0223
##    280        1.0550             nan     0.1000   -0.0132
##    300        0.9905             nan     0.1000   -0.0189
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.6111             nan     0.0100    0.7336
##      2       59.9035             nan     0.0100    0.7284
##      3       59.1369             nan     0.0100    0.6839
##      4       58.4266             nan     0.0100    0.7272
##      5       57.7643             nan     0.0100    0.6231
##      6       57.0606             nan     0.0100    0.6825
##      7       56.3533             nan     0.0100    0.6824
##      8       55.7011             nan     0.0100    0.6267
##      9       55.0193             nan     0.0100    0.6522
##     10       54.2865             nan     0.0100    0.6374
##     20       48.3009             nan     0.0100    0.5510
##     40       38.8594             nan     0.0100    0.3557
##     60       32.0326             nan     0.0100    0.2656
##     80       26.5393             nan     0.0100    0.2287
##    100       22.4845             nan     0.0100    0.1519
##    120       19.1429             nan     0.0100    0.1216
##    140       16.6190             nan     0.0100    0.1101
##    160       14.5774             nan     0.0100    0.0796
##    180       12.9194             nan     0.0100    0.0660
##    200       11.5409             nan     0.0100    0.0456
##    220       10.3951             nan     0.0100    0.0489
##    240        9.4656             nan     0.0100    0.0342
##    260        8.6582             nan     0.0100    0.0327
##    280        7.9663             nan     0.0100    0.0168
##    300        7.3633             nan     0.0100    0.0260
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.6813             nan     0.0100    0.7310
##      2       59.9931             nan     0.0100    0.7730
##      3       59.2090             nan     0.0100    0.7390
##      4       58.4632             nan     0.0100    0.7210
##      5       57.6812             nan     0.0100    0.7051
##      6       56.9632             nan     0.0100    0.7568
##      7       56.2857             nan     0.0100    0.7171
##      8       55.6027             nan     0.0100    0.6815
##      9       55.0002             nan     0.0100    0.6361
##     10       54.3810             nan     0.0100    0.6466
##     20       48.4453             nan     0.0100    0.5504
##     40       38.9750             nan     0.0100    0.3946
##     60       32.0457             nan     0.0100    0.3000
##     80       26.6680             nan     0.0100    0.1879
##    100       22.5329             nan     0.0100    0.1950
##    120       19.3149             nan     0.0100    0.1066
##    140       16.7343             nan     0.0100    0.1035
##    160       14.6880             nan     0.0100    0.1041
##    180       12.9930             nan     0.0100    0.0655
##    200       11.6174             nan     0.0100    0.0573
##    220       10.5061             nan     0.0100    0.0399
##    240        9.5584             nan     0.0100    0.0358
##    260        8.7304             nan     0.0100    0.0296
##    280        8.0585             nan     0.0100    0.0154
##    300        7.4698             nan     0.0100    0.0220
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.6210             nan     0.0100    0.7643
##      2       59.9189             nan     0.0100    0.7546
##      3       59.2374             nan     0.0100    0.7288
##      4       58.4934             nan     0.0100    0.7229
##      5       57.7318             nan     0.0100    0.7413
##      6       57.0263             nan     0.0100    0.7308
##      7       56.3427             nan     0.0100    0.6451
##      8       55.6939             nan     0.0100    0.7285
##      9       55.0414             nan     0.0100    0.6567
##     10       54.3798             nan     0.0100    0.6393
##     20       48.3319             nan     0.0100    0.5471
##     40       38.8705             nan     0.0100    0.3552
##     60       31.8643             nan     0.0100    0.2721
##     80       26.5174             nan     0.0100    0.2332
##    100       22.4726             nan     0.0100    0.1578
##    120       19.1952             nan     0.0100    0.1279
##    140       16.7255             nan     0.0100    0.0919
##    160       14.6642             nan     0.0100    0.0679
##    180       13.0349             nan     0.0100    0.0511
##    200       11.7320             nan     0.0100    0.0558
##    220       10.6177             nan     0.0100    0.0514
##    240        9.6862             nan     0.0100    0.0366
##    260        8.9201             nan     0.0100    0.0354
##    280        8.2260             nan     0.0100    0.0205
##    300        7.6659             nan     0.0100    0.0220
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.3664             nan     0.0100    0.9490
##      2       59.4029             nan     0.0100    0.9511
##      3       58.4293             nan     0.0100    0.9291
##      4       57.4619             nan     0.0100    1.0679
##      5       56.4938             nan     0.0100    0.9267
##      6       55.5583             nan     0.0100    0.8971
##      7       54.5650             nan     0.0100    1.0088
##      8       53.6148             nan     0.0100    0.7895
##      9       52.7555             nan     0.0100    0.8632
##     10       51.9086             nan     0.0100    0.7475
##     20       44.1231             nan     0.0100    0.6960
##     40       32.2861             nan     0.0100    0.4852
##     60       24.1975             nan     0.0100    0.3348
##     80       18.4346             nan     0.0100    0.2284
##    100       14.3806             nan     0.0100    0.1761
##    120       11.5370             nan     0.0100    0.1153
##    140        9.4902             nan     0.0100    0.0971
##    160        7.9619             nan     0.0100    0.0696
##    180        6.8200             nan     0.0100    0.0453
##    200        5.9702             nan     0.0100    0.0279
##    220        5.3070             nan     0.0100    0.0144
##    240        4.7880             nan     0.0100    0.0158
##    260        4.3930             nan     0.0100    0.0112
##    280        4.1102             nan     0.0100    0.0004
##    300        3.8631             nan     0.0100    0.0028
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.3441             nan     0.0100    1.0884
##      2       59.3898             nan     0.0100    0.9820
##      3       58.4082             nan     0.0100    0.8843
##      4       57.4675             nan     0.0100    0.9217
##      5       56.5249             nan     0.0100    0.8988
##      6       55.6565             nan     0.0100    0.8703
##      7       54.6882             nan     0.0100    1.0201
##      8       53.7920             nan     0.0100    0.9010
##      9       52.8560             nan     0.0100    0.8745
##     10       51.9757             nan     0.0100    0.8403
##     20       44.1865             nan     0.0100    0.7569
##     40       32.4272             nan     0.0100    0.5145
##     60       24.2446             nan     0.0100    0.3711
##     80       18.4656             nan     0.0100    0.2337
##    100       14.5886             nan     0.0100    0.1065
##    120       11.7090             nan     0.0100    0.1212
##    140        9.6358             nan     0.0100    0.0848
##    160        8.0676             nan     0.0100    0.0557
##    180        6.9297             nan     0.0100    0.0340
##    200        6.0792             nan     0.0100    0.0228
##    220        5.4162             nan     0.0100    0.0264
##    240        4.9316             nan     0.0100    0.0122
##    260        4.5482             nan     0.0100    0.0079
##    280        4.2537             nan     0.0100    0.0042
##    300        4.0187             nan     0.0100    0.0046
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.3500             nan     0.0100    1.0190
##      2       59.3603             nan     0.0100    0.9367
##      3       58.4078             nan     0.0100    0.8853
##      4       57.3704             nan     0.0100    0.9471
##      5       56.4431             nan     0.0100    0.9108
##      6       55.5651             nan     0.0100    0.8060
##      7       54.6937             nan     0.0100    0.9691
##      8       53.7700             nan     0.0100    0.9101
##      9       52.8925             nan     0.0100    0.8669
##     10       52.0657             nan     0.0100    0.8167
##     20       44.3176             nan     0.0100    0.7018
##     40       32.4732             nan     0.0100    0.4926
##     60       24.3577             nan     0.0100    0.3101
##     80       18.7292             nan     0.0100    0.2253
##    100       14.7540             nan     0.0100    0.1218
##    120       11.9618             nan     0.0100    0.1111
##    140        9.8608             nan     0.0100    0.0783
##    160        8.3147             nan     0.0100    0.0568
##    180        7.1793             nan     0.0100    0.0443
##    200        6.2955             nan     0.0100    0.0220
##    220        5.6613             nan     0.0100    0.0207
##    240        5.1533             nan     0.0100    0.0164
##    260        4.7771             nan     0.0100    0.0100
##    280        4.4948             nan     0.0100    0.0078
##    300        4.2677             nan     0.0100    0.0028
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.2489             nan     0.0100    1.1364
##      2       59.1998             nan     0.0100    1.0307
##      3       58.0793             nan     0.0100    1.1226
##      4       57.0762             nan     0.0100    0.8950
##      5       56.0592             nan     0.0100    1.0230
##      6       55.0295             nan     0.0100    1.0202
##      7       54.1311             nan     0.0100    0.9347
##      8       53.2610             nan     0.0100    0.8762
##      9       52.3056             nan     0.0100    0.8626
##     10       51.3837             nan     0.0100    0.8446
##     20       43.2125             nan     0.0100    0.7242
##     40       31.1719             nan     0.0100    0.4753
##     60       22.8254             nan     0.0100    0.2939
##     80       17.0122             nan     0.0100    0.2435
##    100       13.0349             nan     0.0100    0.1575
##    120       10.1533             nan     0.0100    0.1000
##    140        8.1657             nan     0.0100    0.0674
##    160        6.6953             nan     0.0100    0.0468
##    180        5.6460             nan     0.0100    0.0283
##    200        4.8689             nan     0.0100    0.0279
##    220        4.2930             nan     0.0100    0.0172
##    240        3.8906             nan     0.0100    0.0077
##    260        3.5652             nan     0.0100    0.0043
##    280        3.3217             nan     0.0100    0.0044
##    300        3.1260             nan     0.0100    0.0025
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.2968             nan     0.0100    1.1529
##      2       59.2411             nan     0.0100    1.0693
##      3       58.2234             nan     0.0100    0.9467
##      4       57.1841             nan     0.0100    0.9436
##      5       56.2098             nan     0.0100    0.9415
##      6       55.2144             nan     0.0100    0.9335
##      7       54.3086             nan     0.0100    0.9898
##      8       53.3484             nan     0.0100    0.9354
##      9       52.4844             nan     0.0100    0.9183
##     10       51.5769             nan     0.0100    0.9541
##     20       43.3390             nan     0.0100    0.7148
##     40       31.0169             nan     0.0100    0.5128
##     60       22.7752             nan     0.0100    0.3130
##     80       17.0462             nan     0.0100    0.2339
##    100       13.0226             nan     0.0100    0.1655
##    120       10.1888             nan     0.0100    0.0911
##    140        8.1973             nan     0.0100    0.0754
##    160        6.7405             nan     0.0100    0.0550
##    180        5.7111             nan     0.0100    0.0302
##    200        4.9774             nan     0.0100    0.0258
##    220        4.4524             nan     0.0100    0.0058
##    240        4.0628             nan     0.0100    0.0133
##    260        3.7658             nan     0.0100    0.0100
##    280        3.5400             nan     0.0100    0.0055
##    300        3.3572             nan     0.0100   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       60.3088             nan     0.0100    0.9468
##      2       59.2464             nan     0.0100    1.0670
##      3       58.2532             nan     0.0100    0.9555
##      4       57.2127             nan     0.0100    0.9789
##      5       56.2379             nan     0.0100    0.8822
##      6       55.2641             nan     0.0100    1.0158
##      7       54.3385             nan     0.0100    0.9156
##      8       53.4357             nan     0.0100    0.8923
##      9       52.5017             nan     0.0100    0.9272
##     10       51.6417             nan     0.0100    0.8443
##     20       43.5650             nan     0.0100    0.7546
##     40       31.5142             nan     0.0100    0.4929
##     60       23.1920             nan     0.0100    0.3233
##     80       17.4470             nan     0.0100    0.1912
##    100       13.4063             nan     0.0100    0.1669
##    120       10.5748             nan     0.0100    0.1018
##    140        8.5803             nan     0.0100    0.0700
##    160        7.1628             nan     0.0100    0.0424
##    180        6.1542             nan     0.0100    0.0324
##    200        5.4242             nan     0.0100    0.0271
##    220        4.8959             nan     0.0100    0.0172
##    240        4.5136             nan     0.0100    0.0071
##    260        4.2139             nan     0.0100    0.0060
##    280        3.9963             nan     0.0100   -0.0015
##    300        3.8197             nan     0.0100   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.7044             nan     0.0500    3.6068
##      2       54.4860             nan     0.0500    3.6038
##      3       51.4795             nan     0.0500    3.0725
##      4       48.2829             nan     0.0500    2.7477
##      5       45.6534             nan     0.0500    2.4187
##      6       43.2131             nan     0.0500    2.4606
##      7       41.1646             nan     0.0500    2.1205
##      8       38.8947             nan     0.0500    2.1821
##      9       36.9404             nan     0.0500    1.8426
##     10       35.1515             nan     0.0500    1.5676
##     20       22.1450             nan     0.0500    0.8063
##     40       11.6054             nan     0.0500    0.2089
##     60        7.5295             nan     0.0500    0.0846
##     80        5.5828             nan     0.0500    0.0200
##    100        4.7260             nan     0.0500    0.0165
##    120        4.3173             nan     0.0500    0.0070
##    140        4.0960             nan     0.0500   -0.0066
##    160        3.9644             nan     0.0500   -0.0041
##    180        3.8590             nan     0.0500   -0.0016
##    200        3.7709             nan     0.0500   -0.0088
##    220        3.7133             nan     0.0500   -0.0113
##    240        3.6451             nan     0.0500   -0.0130
##    260        3.5943             nan     0.0500   -0.0192
##    280        3.5529             nan     0.0500   -0.0192
##    300        3.5120             nan     0.0500   -0.0054
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.7122             nan     0.0500    3.5301
##      2       54.1298             nan     0.0500    3.4151
##      3       51.0843             nan     0.0500    3.1384
##      4       48.2779             nan     0.0500    2.8377
##      5       45.6377             nan     0.0500    2.5901
##      6       43.3251             nan     0.0500    2.3360
##      7       41.0335             nan     0.0500    2.3503
##      8       38.8456             nan     0.0500    2.0521
##      9       36.9219             nan     0.0500    1.8290
##     10       35.2080             nan     0.0500    1.6629
##     20       22.3268             nan     0.0500    0.9230
##     40       11.4470             nan     0.0500    0.2404
##     60        7.4390             nan     0.0500    0.1025
##     80        5.6278             nan     0.0500    0.0516
##    100        4.7574             nan     0.0500    0.0200
##    120        4.3662             nan     0.0500   -0.0082
##    140        4.1667             nan     0.0500   -0.0029
##    160        4.0477             nan     0.0500    0.0005
##    180        3.9309             nan     0.0500   -0.0052
##    200        3.8595             nan     0.0500   -0.0194
##    220        3.7828             nan     0.0500   -0.0028
##    240        3.7262             nan     0.0500   -0.0055
##    260        3.6650             nan     0.0500   -0.0101
##    280        3.6227             nan     0.0500   -0.0072
##    300        3.5902             nan     0.0500   -0.0080
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       57.5812             nan     0.0500    3.8763
##      2       54.3611             nan     0.0500    3.3412
##      3       51.1497             nan     0.0500    3.0675
##      4       48.3818             nan     0.0500    3.0605
##      5       45.7192             nan     0.0500    2.6437
##      6       43.3694             nan     0.0500    2.3816
##      7       40.9379             nan     0.0500    2.3437
##      8       38.8622             nan     0.0500    2.0155
##      9       36.8831             nan     0.0500    1.6641
##     10       35.2245             nan     0.0500    1.7334
##     20       22.5093             nan     0.0500    0.8259
##     40       11.6596             nan     0.0500    0.3277
##     60        7.6610             nan     0.0500    0.0858
##     80        5.8494             nan     0.0500    0.0685
##    100        4.9810             nan     0.0500    0.0095
##    120        4.6103             nan     0.0500    0.0128
##    140        4.4076             nan     0.0500    0.0105
##    160        4.2791             nan     0.0500   -0.0190
##    180        4.1837             nan     0.0500   -0.0064
##    200        4.0930             nan     0.0500    0.0001
##    220        4.0244             nan     0.0500   -0.0168
##    240        3.9636             nan     0.0500   -0.0074
##    260        3.8951             nan     0.0500   -0.0096
##    280        3.8323             nan     0.0500   -0.0074
##    300        3.7825             nan     0.0500   -0.0038
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.2194             nan     0.0500    4.6312
##      2       51.9347             nan     0.0500    4.9296
##      3       48.0273             nan     0.0500    3.7486
##      4       44.2328             nan     0.0500    4.1860
##      5       40.7202             nan     0.0500    3.5817
##      6       37.6699             nan     0.0500    3.2747
##      7       34.9171             nan     0.0500    2.4521
##      8       32.3180             nan     0.0500    2.6075
##      9       29.9564             nan     0.0500    2.0336
##     10       27.7736             nan     0.0500    2.2388
##     20       14.2743             nan     0.0500    0.7548
##     40        6.0720             nan     0.0500    0.1678
##     60        3.9432             nan     0.0500    0.0556
##     80        3.3035             nan     0.0500   -0.0064
##    100        2.9980             nan     0.0500   -0.0194
##    120        2.7428             nan     0.0500   -0.0093
##    140        2.5584             nan     0.0500   -0.0061
##    160        2.4108             nan     0.0500   -0.0224
##    180        2.2766             nan     0.0500    0.0003
##    200        2.1684             nan     0.0500   -0.0157
##    220        2.0407             nan     0.0500   -0.0073
##    240        1.9323             nan     0.0500   -0.0095
##    260        1.8425             nan     0.0500   -0.0126
##    280        1.7615             nan     0.0500   -0.0163
##    300        1.6895             nan     0.0500   -0.0048
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.3794             nan     0.0500    5.0040
##      2       51.8961             nan     0.0500    4.4267
##      3       47.6231             nan     0.0500    3.5971
##      4       43.7211             nan     0.0500    3.5671
##      5       40.1669             nan     0.0500    3.5921
##      6       37.2068             nan     0.0500    2.4737
##      7       34.2648             nan     0.0500    2.6141
##      8       31.5883             nan     0.0500    2.5376
##      9       29.3471             nan     0.0500    2.1549
##     10       27.1261             nan     0.0500    2.3840
##     20       14.1624             nan     0.0500    0.7217
##     40        5.9939             nan     0.0500    0.1748
##     60        4.0245             nan     0.0500    0.0138
##     80        3.4240             nan     0.0500   -0.0055
##    100        3.1602             nan     0.0500    0.0083
##    120        2.9570             nan     0.0500   -0.0177
##    140        2.8229             nan     0.0500   -0.0174
##    160        2.6943             nan     0.0500   -0.0105
##    180        2.5606             nan     0.0500   -0.0099
##    200        2.4451             nan     0.0500   -0.0275
##    220        2.3247             nan     0.0500   -0.0113
##    240        2.2328             nan     0.0500   -0.0154
##    260        2.1604             nan     0.0500   -0.0140
##    280        2.0837             nan     0.0500   -0.0073
##    300        1.9897             nan     0.0500   -0.0100
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.1751             nan     0.0500    4.7242
##      2       51.9385             nan     0.0500    4.5791
##      3       47.7678             nan     0.0500    4.2664
##      4       43.9528             nan     0.0500    4.2168
##      5       40.4708             nan     0.0500    3.6818
##      6       37.4278             nan     0.0500    2.9515
##      7       34.7079             nan     0.0500    2.3996
##      8       32.2776             nan     0.0500    2.4402
##      9       30.0835             nan     0.0500    1.9938
##     10       27.9190             nan     0.0500    2.0398
##     20       14.5739             nan     0.0500    0.8620
##     40        6.4316             nan     0.0500    0.1544
##     60        4.4438             nan     0.0500    0.0307
##     80        3.8964             nan     0.0500   -0.0230
##    100        3.5835             nan     0.0500   -0.0383
##    120        3.3612             nan     0.0500   -0.0109
##    140        3.1757             nan     0.0500   -0.0074
##    160        2.9966             nan     0.0500   -0.0189
##    180        2.8774             nan     0.0500   -0.0184
##    200        2.7498             nan     0.0500   -0.0100
##    220        2.6249             nan     0.0500   -0.0238
##    240        2.5440             nan     0.0500   -0.0091
##    260        2.4550             nan     0.0500   -0.0088
##    280        2.3690             nan     0.0500   -0.0134
##    300        2.2901             nan     0.0500   -0.0115
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.3137             nan     0.0500    5.1372
##      2       51.5330             nan     0.0500    4.5945
##      3       47.2745             nan     0.0500    3.9988
##      4       43.3392             nan     0.0500    4.2006
##      5       39.7695             nan     0.0500    3.4859
##      6       36.5980             nan     0.0500    2.9833
##      7       33.7490             nan     0.0500    2.7244
##      8       31.0272             nan     0.0500    2.4015
##      9       28.5711             nan     0.0500    2.3339
##     10       26.4512             nan     0.0500    2.4860
##     20       12.6775             nan     0.0500    0.8647
##     40        4.6457             nan     0.0500    0.1575
##     60        3.0133             nan     0.0500   -0.0072
##     80        2.4535             nan     0.0500   -0.0168
##    100        2.1399             nan     0.0500   -0.0072
##    120        1.8872             nan     0.0500   -0.0139
##    140        1.7178             nan     0.0500   -0.0146
##    160        1.5734             nan     0.0500   -0.0093
##    180        1.4478             nan     0.0500   -0.0035
##    200        1.3158             nan     0.0500   -0.0177
##    220        1.2101             nan     0.0500   -0.0173
##    240        1.1332             nan     0.0500   -0.0058
##    260        1.0541             nan     0.0500   -0.0068
##    280        0.9790             nan     0.0500   -0.0072
##    300        0.9100             nan     0.0500   -0.0064
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.0808             nan     0.0500    5.5010
##      2       51.2346             nan     0.0500    4.4403
##      3       46.8758             nan     0.0500    4.1584
##      4       42.9460             nan     0.0500    3.6832
##      5       39.3608             nan     0.0500    3.3673
##      6       36.1963             nan     0.0500    3.4394
##      7       33.3486             nan     0.0500    3.1439
##      8       30.8008             nan     0.0500    2.6522
##      9       28.3031             nan     0.0500    2.2201
##     10       26.1856             nan     0.0500    2.0856
##     20       12.9055             nan     0.0500    0.8579
##     40        5.0199             nan     0.0500    0.1273
##     60        3.3143             nan     0.0500   -0.0143
##     80        2.7825             nan     0.0500   -0.0033
##    100        2.4598             nan     0.0500   -0.0247
##    120        2.2223             nan     0.0500   -0.0133
##    140        2.0390             nan     0.0500   -0.0197
##    160        1.8793             nan     0.0500   -0.0246
##    180        1.7536             nan     0.0500   -0.0106
##    200        1.6556             nan     0.0500   -0.0060
##    220        1.5493             nan     0.0500   -0.0103
##    240        1.4502             nan     0.0500   -0.0178
##    260        1.3722             nan     0.0500   -0.0050
##    280        1.3022             nan     0.0500   -0.0065
##    300        1.2402             nan     0.0500   -0.0112
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       56.1240             nan     0.0500    4.9851
##      2       51.5271             nan     0.0500    4.7105
##      3       47.2978             nan     0.0500    3.9636
##      4       43.2374             nan     0.0500    3.9442
##      5       39.6017             nan     0.0500    3.4928
##      6       36.5024             nan     0.0500    2.7629
##      7       33.7784             nan     0.0500    2.6104
##      8       31.1471             nan     0.0500    2.8854
##      9       28.7282             nan     0.0500    2.4637
##     10       26.5387             nan     0.0500    2.0653
##     20       13.1396             nan     0.0500    0.7494
##     40        5.2301             nan     0.0500    0.0377
##     60        3.8169             nan     0.0500    0.0036
##     80        3.3373             nan     0.0500   -0.0175
##    100        3.0738             nan     0.0500   -0.0143
##    120        2.8389             nan     0.0500   -0.0086
##    140        2.6198             nan     0.0500   -0.0253
##    160        2.4581             nan     0.0500   -0.0117
##    180        2.3145             nan     0.0500   -0.0014
##    200        2.1866             nan     0.0500   -0.0103
##    220        2.0803             nan     0.0500   -0.0039
##    240        1.9756             nan     0.0500   -0.0105
##    260        1.8827             nan     0.0500   -0.0068
##    280        1.8105             nan     0.0500   -0.0106
##    300        1.7249             nan     0.0500   -0.0172
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       53.6591             nan     0.1000    6.7715
##      2       48.3010             nan     0.1000    5.7867
##      3       42.4891             nan     0.1000    5.9609
##      4       38.2250             nan     0.1000    3.8666
##      5       33.9413             nan     0.1000    3.7782
##      6       30.6817             nan     0.1000    3.3427
##      7       27.9472             nan     0.1000    2.6073
##      8       25.7814             nan     0.1000    1.9178
##      9       23.2969             nan     0.1000    2.3313
##     10       21.4272             nan     0.1000    1.2739
##     20       11.5102             nan     0.1000    0.5290
##     40        5.5209             nan     0.1000    0.0758
##     60        4.2664             nan     0.1000   -0.0266
##     80        3.8975             nan     0.1000   -0.0090
##    100        3.7673             nan     0.1000   -0.0295
##    120        3.6611             nan     0.1000   -0.0328
##    140        3.5743             nan     0.1000   -0.0148
##    160        3.4770             nan     0.1000   -0.0314
##    180        3.3889             nan     0.1000    0.0021
##    200        3.3110             nan     0.1000    0.0021
##    220        3.2260             nan     0.1000   -0.0147
##    240        3.1853             nan     0.1000   -0.0135
##    260        3.1164             nan     0.1000   -0.0132
##    280        3.0687             nan     0.1000   -0.0172
##    300        3.0184             nan     0.1000   -0.0042
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.0675             nan     0.1000    7.7076
##      2       47.5763             nan     0.1000    5.7988
##      3       42.2040             nan     0.1000    5.5087
##      4       37.9913             nan     0.1000    4.1518
##      5       34.5586             nan     0.1000    3.4527
##      6       31.3010             nan     0.1000    3.1918
##      7       28.3196             nan     0.1000    2.8274
##      8       25.8142             nan     0.1000    2.2632
##      9       23.7193             nan     0.1000    2.1536
##     10       21.6919             nan     0.1000    1.5309
##     20       11.3291             nan     0.1000    0.5633
##     40        5.7202             nan     0.1000    0.1444
##     60        4.3753             nan     0.1000    0.0114
##     80        4.0448             nan     0.1000   -0.0121
##    100        3.8701             nan     0.1000   -0.0063
##    120        3.7362             nan     0.1000   -0.0180
##    140        3.6115             nan     0.1000   -0.0079
##    160        3.5389             nan     0.1000   -0.0043
##    180        3.4667             nan     0.1000   -0.0047
##    200        3.3945             nan     0.1000   -0.0409
##    220        3.3553             nan     0.1000   -0.0122
##    240        3.3044             nan     0.1000   -0.0302
##    260        3.2371             nan     0.1000   -0.0126
##    280        3.1889             nan     0.1000   -0.0074
##    300        3.1440             nan     0.1000   -0.0174
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       54.0901             nan     0.1000    7.5111
##      2       47.7363             nan     0.1000    5.7829
##      3       42.5440             nan     0.1000    4.8537
##      4       38.7286             nan     0.1000    3.9717
##      5       34.9872             nan     0.1000    3.2201
##      6       31.5318             nan     0.1000    3.1716
##      7       28.2819             nan     0.1000    2.8026
##      8       25.9236             nan     0.1000    2.4033
##      9       23.6226             nan     0.1000    2.1979
##     10       22.0039             nan     0.1000    1.4336
##     20       11.7075             nan     0.1000    0.3756
##     40        6.0787             nan     0.1000    0.0725
##     60        4.7798             nan     0.1000   -0.0153
##     80        4.4986             nan     0.1000   -0.0529
##    100        4.2960             nan     0.1000   -0.0206
##    120        4.1212             nan     0.1000    0.0028
##    140        3.9919             nan     0.1000   -0.0649
##    160        3.8405             nan     0.1000   -0.0372
##    180        3.7531             nan     0.1000   -0.0065
##    200        3.6914             nan     0.1000   -0.0238
##    220        3.6297             nan     0.1000   -0.0080
##    240        3.5573             nan     0.1000   -0.0091
##    260        3.5090             nan     0.1000   -0.0045
##    280        3.4254             nan     0.1000   -0.0096
##    300        3.3711             nan     0.1000   -0.0162
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.6544             nan     0.1000   10.0984
##      2       43.7615             nan     0.1000    6.5339
##      3       36.8242             nan     0.1000    6.1128
##      4       31.6833             nan     0.1000    5.6614
##      5       27.0822             nan     0.1000    4.5967
##      6       23.7313             nan     0.1000    3.4297
##      7       20.8276             nan     0.1000    2.7725
##      8       18.2024             nan     0.1000    2.6371
##      9       16.2313             nan     0.1000    2.1914
##     10       14.2079             nan     0.1000    1.5638
##     20        5.8131             nan     0.1000    0.2346
##     40        3.3491             nan     0.1000   -0.0282
##     60        2.7920             nan     0.1000   -0.0128
##     80        2.4312             nan     0.1000   -0.0156
##    100        2.1622             nan     0.1000   -0.0418
##    120        1.9378             nan     0.1000   -0.0372
##    140        1.7871             nan     0.1000   -0.0262
##    160        1.6445             nan     0.1000   -0.0153
##    180        1.5066             nan     0.1000   -0.0165
##    200        1.3807             nan     0.1000   -0.0146
##    220        1.2761             nan     0.1000   -0.0124
##    240        1.1967             nan     0.1000   -0.0102
##    260        1.1261             nan     0.1000   -0.0059
##    280        1.0537             nan     0.1000   -0.0243
##    300        0.9882             nan     0.1000   -0.0068
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.4786             nan     0.1000    9.7103
##      2       43.5594             nan     0.1000    8.0911
##      3       36.9666             nan     0.1000    6.6066
##      4       31.9142             nan     0.1000    6.0019
##      5       27.5164             nan     0.1000    4.0046
##      6       23.7718             nan     0.1000    3.4984
##      7       20.7906             nan     0.1000    2.8973
##      8       18.1682             nan     0.1000    2.4754
##      9       16.2047             nan     0.1000    1.8840
##     10       14.1921             nan     0.1000    1.8087
##     20        6.1461             nan     0.1000    0.3012
##     40        3.4691             nan     0.1000   -0.0183
##     60        2.9709             nan     0.1000   -0.0594
##     80        2.6037             nan     0.1000   -0.0039
##    100        2.3855             nan     0.1000   -0.0399
##    120        2.1780             nan     0.1000   -0.0203
##    140        1.9992             nan     0.1000   -0.0121
##    160        1.8866             nan     0.1000   -0.0140
##    180        1.7876             nan     0.1000   -0.0352
##    200        1.6674             nan     0.1000   -0.0230
##    220        1.5755             nan     0.1000   -0.0183
##    240        1.4904             nan     0.1000   -0.0258
##    260        1.4021             nan     0.1000   -0.0199
##    280        1.3356             nan     0.1000   -0.0088
##    300        1.2642             nan     0.1000   -0.0169
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.7884             nan     0.1000    9.6041
##      2       43.5082             nan     0.1000    7.7349
##      3       36.7723             nan     0.1000    6.6486
##      4       31.2501             nan     0.1000    5.0619
##      5       27.4044             nan     0.1000    4.4218
##      6       23.8806             nan     0.1000    3.6108
##      7       20.8837             nan     0.1000    2.8841
##      8       18.4225             nan     0.1000    1.9319
##      9       16.4330             nan     0.1000    1.5314
##     10       14.6498             nan     0.1000    1.7993
##     20        6.1358             nan     0.1000    0.2728
##     40        3.7223             nan     0.1000   -0.0273
##     60        3.2456             nan     0.1000   -0.0638
##     80        2.9398             nan     0.1000   -0.0105
##    100        2.7081             nan     0.1000   -0.0211
##    120        2.5264             nan     0.1000   -0.0281
##    140        2.3295             nan     0.1000   -0.0573
##    160        2.1631             nan     0.1000   -0.0171
##    180        2.0247             nan     0.1000   -0.0099
##    200        1.9037             nan     0.1000   -0.0195
##    220        1.8217             nan     0.1000   -0.0149
##    240        1.7207             nan     0.1000   -0.0115
##    260        1.6548             nan     0.1000   -0.0428
##    280        1.5657             nan     0.1000   -0.0149
##    300        1.4950             nan     0.1000   -0.0078
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       50.9588             nan     0.1000    9.4794
##      2       42.5804             nan     0.1000    8.4162
##      3       35.7943             nan     0.1000    6.1026
##      4       30.1986             nan     0.1000    5.0705
##      5       25.4983             nan     0.1000    4.6203
##      6       21.8833             nan     0.1000    3.3288
##      7       18.8696             nan     0.1000    2.6876
##      8       16.1460             nan     0.1000    2.3235
##      9       14.1126             nan     0.1000    1.9322
##     10       12.3574             nan     0.1000    1.5953
##     20        4.7097             nan     0.1000    0.2904
##     40        2.4959             nan     0.1000   -0.0006
##     60        1.9685             nan     0.1000   -0.0520
##     80        1.6370             nan     0.1000   -0.0200
##    100        1.3850             nan     0.1000   -0.0268
##    120        1.1901             nan     0.1000   -0.0074
##    140        1.0546             nan     0.1000   -0.0182
##    160        0.9172             nan     0.1000   -0.0157
##    180        0.7932             nan     0.1000   -0.0106
##    200        0.7076             nan     0.1000   -0.0139
##    220        0.6218             nan     0.1000   -0.0172
##    240        0.5482             nan     0.1000   -0.0112
##    260        0.4917             nan     0.1000   -0.0086
##    280        0.4423             nan     0.1000   -0.0106
##    300        0.4010             nan     0.1000   -0.0053
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.0314             nan     0.1000   10.6135
##      2       42.7274             nan     0.1000    7.7280
##      3       35.9765             nan     0.1000    7.4152
##      4       30.3635             nan     0.1000    5.1352
##      5       25.7286             nan     0.1000    4.1265
##      6       21.8343             nan     0.1000    4.0708
##      7       18.6432             nan     0.1000    3.0269
##      8       16.0085             nan     0.1000    2.4161
##      9       13.8918             nan     0.1000    2.1323
##     10       12.1956             nan     0.1000    1.5724
##     20        4.7421             nan     0.1000    0.2029
##     40        2.7459             nan     0.1000   -0.0239
##     60        2.2100             nan     0.1000   -0.0393
##     80        1.9041             nan     0.1000   -0.0152
##    100        1.6773             nan     0.1000   -0.0312
##    120        1.4970             nan     0.1000   -0.0218
##    140        1.3284             nan     0.1000   -0.0142
##    160        1.1809             nan     0.1000   -0.0247
##    180        1.0333             nan     0.1000   -0.0112
##    200        0.9402             nan     0.1000   -0.0063
##    220        0.8640             nan     0.1000   -0.0118
##    240        0.7870             nan     0.1000   -0.0175
##    260        0.7268             nan     0.1000   -0.0274
##    280        0.6661             nan     0.1000   -0.0077
##    300        0.6106             nan     0.1000   -0.0092
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       51.3636             nan     0.1000   10.1561
##      2       42.7844             nan     0.1000    7.0321
##      3       35.9204             nan     0.1000    6.8687
##      4       30.4760             nan     0.1000    5.3386
##      5       25.7371             nan     0.1000    4.2528
##      6       22.0112             nan     0.1000    3.7777
##      7       18.9485             nan     0.1000    3.0504
##      8       16.4187             nan     0.1000    2.3605
##      9       14.3610             nan     0.1000    1.8215
##     10       12.7530             nan     0.1000    1.6211
##     20        5.2737             nan     0.1000    0.1733
##     40        3.3011             nan     0.1000   -0.0077
##     60        2.8406             nan     0.1000   -0.0487
##     80        2.4461             nan     0.1000   -0.0250
##    100        2.1746             nan     0.1000   -0.0229
##    120        1.9425             nan     0.1000   -0.0073
##    140        1.7949             nan     0.1000   -0.0192
##    160        1.6444             nan     0.1000   -0.0213
##    180        1.5138             nan     0.1000   -0.0053
##    200        1.4078             nan     0.1000   -0.0071
##    220        1.2648             nan     0.1000   -0.0123
##    240        1.1639             nan     0.1000   -0.0140
##    260        1.0858             nan     0.1000   -0.0092
##    280        1.0228             nan     0.1000   -0.0249
##    300        0.9577             nan     0.1000   -0.0125
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1       52.4616             nan     0.1000   10.3905
##      2       44.3583             nan     0.1000    8.1397
##      3       37.4620             nan     0.1000    6.1114
##      4       31.8007             nan     0.1000    5.6530
##      5       27.0376             nan     0.1000    4.5885
##      6       23.3893             nan     0.1000    3.3185
##      7       20.4557             nan     0.1000    2.7501
##      8       17.7660             nan     0.1000    2.5441
##      9       15.7836             nan     0.1000    2.0778
##     10       14.0228             nan     0.1000    1.7046
##     20        5.9281             nan     0.1000    0.2932
##     40        3.4548             nan     0.1000   -0.0049
##     60        2.9358             nan     0.1000   -0.0250
##     80        2.6003             nan     0.1000   -0.0225
##    100        2.3949             nan     0.1000   -0.0240
##    120        2.1777             nan     0.1000   -0.0255
##    140        2.0366             nan     0.1000   -0.0287
##    160        1.8911             nan     0.1000   -0.0440
##    180        1.7667             nan     0.1000   -0.0167
##    200        1.6584             nan     0.1000   -0.0249
##################################
# Reporting the apparent results
# for the GBM model
##################################
GBM_DALEX <- DALEX::explain(GBM_Tune,
                            data = MD.Model.Predictors,
                            y = MD$LIFEXP,
                            verbose = FALSE,
                            label = "GBM")

(GBM_DALEX_Performance <- model_performance(GBM_DALEX))
## Measures for:  regression
## mse        : 1.658418 
## rmse       : 1.287796 
## r2         : 0.9732008 
## mad        : 0.7400697
## 
## Residuals:
##          0%         10%         20%         30%         40%         50% 
## -4.73887723 -1.34111721 -0.83535168 -0.49339743 -0.22544393 -0.05181421 
##         60%         70%         80%         90%        100% 
##  0.16238009  0.52825030  0.91403922  1.67095243  4.33309954
(GBM_DALEX_Diagnostics <- model_diagnostics(GBM_DALEX))
##     GENDER              CONTIN       INFMOR           PERCAP       
##  Male  :139   Africa       :89   Min.   :0.3365   Min.   :-1.4775  
##  Female:153   Asia         :73   1st Qu.:1.7047   1st Qu.: 0.6183  
##               Europe       :62   Median :2.6100   Median : 1.7960  
##               North America:31   Mean   :2.5569   Mean   : 1.7571  
##               Oceania      :18   3rd Qu.:3.5025   3rd Qu.: 2.7938  
##               South America:19   Max.   :4.4864   Max.   : 4.7293  
##      CLTECH           NCOMOR            y             y_hat      
##  Min.   :  0.00   Min.   :1.866   Min.   :52.84   Min.   :55.18  
##  1st Qu.: 24.35   1st Qu.:3.944   1st Qu.:66.93   1st Qu.:66.96  
##  Median : 82.80   Median :4.717   Median :73.53   Median :73.23  
##  Mean   : 64.60   Mean   :4.652   Mean   :72.47   Mean   :72.45  
##  3rd Qu.:100.00   3rd Qu.:5.347   3rd Qu.:78.54   3rd Qu.:78.71  
##  Max.   :100.00   Max.   :7.959   Max.   :87.45   Max.   :84.94  
##    residuals        abs_residuals        label                ids        
##  Min.   :-4.73888   Min.   :0.00415   Length:292         Min.   :  1.00  
##  1st Qu.:-0.70603   1st Qu.:0.27696   Class :character   1st Qu.: 73.75  
##  Median :-0.05181   Median :0.74007   Mode  :character   Median :146.50  
##  Mean   : 0.02483   Mean   :0.94179                      Mean   :146.50  
##  3rd Qu.: 0.75568   3rd Qu.:1.29372                      3rd Qu.:219.25  
##  Max.   : 4.33310   Max.   :4.73888                      Max.   :292.00
plot(GBM_DALEX_Diagnostics,
     variable = "y",
     yvariable = "y_hat") +
  geom_point(size=3) +
  scale_x_continuous("Observed LIFEXP") +
  scale_y_continuous("Predicted LIFEXP") +
  geom_abline(slope = 1) +
  ggtitle("GBM: Observed and Predicted LIFEXP")

GBM_DALEX_VariableImportance    <- model_parts(GBM_DALEX,
                                               loss_function = loss_root_mean_square,
                                               B = 200,
                                               N = NULL)

plot(GBM_DALEX_VariableImportance)

##################################
# Reporting the cross-validation results
# for the GBM model
##################################
GBM_Tune
## Stochastic Gradient Boosting 
## 
## 292 samples
##   6 predictor
## 
## No pre-processing
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 264, 263, 263, 262, 263, 264, ... 
## Resampling results across tuning parameters:
## 
##   shrinkage  interaction.depth  n.minobsinnode  n.trees  RMSE      Rsquared 
##   0.01       1                   5              100      4.772676  0.8333691
##   0.01       1                   5              200      3.515429  0.8726404
##   0.01       1                   5              300      2.877624  0.8964547
##   0.01       1                  10              100      4.793621  0.8332069
##   0.01       1                  10              200      3.521911  0.8733517
##   0.01       1                  10              300      2.890117  0.8956765
##   0.01       1                  15              100      4.793589  0.8339653
##   0.01       1                  15              200      3.530219  0.8713678
##   0.01       1                  15              300      2.904276  0.8956071
##   0.01       3                   5              100      3.892040  0.8947209
##   0.01       3                   5              200      2.654034  0.9139912
##   0.01       3                   5              300      2.264173  0.9235081
##   0.01       3                  10              100      3.896555  0.8970888
##   0.01       3                  10              200      2.650019  0.9149851
##   0.01       3                  10              300      2.270424  0.9234616
##   0.01       3                  15              100      3.936460  0.8916345
##   0.01       3                  15              200      2.697619  0.9125729
##   0.01       3                  15              300      2.311237  0.9215290
##   0.01       5                   5              100      3.698565  0.9106552
##   0.01       5                   5              200      2.476083  0.9228052
##   0.01       5                   5              300      2.189965  0.9274235
##   0.01       5                  10              100      3.694360  0.9124533
##   0.01       5                  10              200      2.483889  0.9223152
##   0.01       5                  10              300      2.188089  0.9272777
##   0.01       5                  15              100      3.743395  0.9073428
##   0.01       5                  15              200      2.537516  0.9193448
##   0.01       5                  15              300      2.252018  0.9238906
##   0.05       1                   5              100      2.348301  0.9165106
##   0.05       1                   5              200      2.164617  0.9255460
##   0.05       1                   5              300      2.145105  0.9272272
##   0.05       1                  10              100      2.371539  0.9151106
##   0.05       1                  10              200      2.192438  0.9241736
##   0.05       1                  10              300      2.175619  0.9254071
##   0.05       1                  15              100      2.418639  0.9118379
##   0.05       1                  15              200      2.246551  0.9215537
##   0.05       1                  15              300      2.210928  0.9234890
##   0.05       3                   5              100      2.132971  0.9279468
##   0.05       3                   5              200      2.128585  0.9281739
##   0.05       3                   5              300      2.138451  0.9271950
##   0.05       3                  10              100      2.151650  0.9270914
##   0.05       3                  10              200      2.134122  0.9280313
##   0.05       3                  10              300      2.140481  0.9273580
##   0.05       3                  15              100      2.210260  0.9236464
##   0.05       3                  15              200      2.175687  0.9253416
##   0.05       3                  15              300      2.171212  0.9250317
##   0.05       5                   5              100      2.183111  0.9251933
##   0.05       5                   5              200      2.197547  0.9232986
##   0.05       5                   5              300      2.192592  0.9235604
##   0.05       5                  10              100      2.125501  0.9289397
##   0.05       5                  10              200      2.148236  0.9267642
##   0.05       5                  10              300      2.149715  0.9262640
##   0.05       5                  15              100      2.160418  0.9266001
##   0.05       5                  15              200      2.173699  0.9253182
##   0.05       5                  15              300      2.169584  0.9248442
##   0.10       1                   5              100      2.216683  0.9224492
##   0.10       1                   5              200      2.171832  0.9249045
##   0.10       1                   5              300      2.163527  0.9254722
##   0.10       1                  10              100      2.190542  0.9240748
##   0.10       1                  10              200      2.135899  0.9276319
##   0.10       1                  10              300      2.132410  0.9273953
##   0.10       1                  15              100      2.303119  0.9177669
##   0.10       1                  15              200      2.211956  0.9236257
##   0.10       1                  15              300      2.191344  0.9244899
##   0.10       3                   5              100      2.165442  0.9258963
##   0.10       3                   5              200      2.157851  0.9261405
##   0.10       3                   5              300      2.165798  0.9247466
##   0.10       3                  10              100      2.131881  0.9280289
##   0.10       3                  10              200      2.107162  0.9285203
##   0.10       3                  10              300      2.135974  0.9258930
##   0.10       3                  15              100      2.169556  0.9263259
##   0.10       3                  15              200      2.161955  0.9250116
##   0.10       3                  15              300      2.145423  0.9254143
##   0.10       5                   5              100      2.193752  0.9240341
##   0.10       5                   5              200      2.214680  0.9221060
##   0.10       5                   5              300      2.218715  0.9214655
##   0.10       5                  10              100      2.212554  0.9219448
##   0.10       5                  10              200      2.239859  0.9195548
##   0.10       5                  10              300      2.261049  0.9178753
##   0.10       5                  15              100      2.189393  0.9242153
##   0.10       5                  15              200      2.205440  0.9225160
##   0.10       5                  15              300      2.194315  0.9223180
##   MAE     
##   3.858956
##   2.840899
##   2.288121
##   3.875879
##   2.845201
##   2.289532
##   3.869335
##   2.827283
##   2.279083
##   3.222475
##   2.104761
##   1.721413
##   3.232783
##   2.091787
##   1.710329
##   3.246277
##   2.109478
##   1.728179
##   3.068383
##   1.947763
##   1.654183
##   3.060625
##   1.945668
##   1.626496
##   3.087076
##   1.962356
##   1.666117
##   1.798163
##   1.599649
##   1.579040
##   1.802411
##   1.633652
##   1.615435
##   1.815861
##   1.661022
##   1.629704
##   1.580361
##   1.572619
##   1.582000
##   1.592463
##   1.563340
##   1.574442
##   1.624147
##   1.596711
##   1.587831
##   1.609724
##   1.604475
##   1.612752
##   1.566812
##   1.581413
##   1.586848
##   1.574829
##   1.585161
##   1.574231
##   1.644726
##   1.591006
##   1.584273
##   1.610851
##   1.562029
##   1.575392
##   1.708563
##   1.627455
##   1.610661
##   1.593192
##   1.608458
##   1.631719
##   1.570714
##   1.559041
##   1.593043
##   1.575222
##   1.593423
##   1.573554
##   1.617333
##   1.630894
##   1.638725
##   1.617015
##   1.634059
##   1.649568
##   1.616787
##   1.619968
##   1.617048
## 
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were n.trees = 200, interaction.depth =
##  3, shrinkage = 0.1 and n.minobsinnode = 10.
GBM_Tune$finalModel
## A gradient boosted model with gaussian loss function.
## 200 iterations were performed.
## There were 6 predictors of which 6 had non-zero influence.
(GBM_Tune_RMSE <- GBM_Tune$results[GBM_Tune$results$shrinkage==GBM_Tune$bestTune$shrinkage &
                              GBM_Tune$results$interaction.depth==GBM_Tune$bestTune$interaction.depth &
                              GBM_Tune$results$n.minobsinnode==GBM_Tune$bestTune$n.minobsinnode &
                              GBM_Tune$results$n.trees==GBM_Tune$bestTune$n.trees,
                 c("RMSE")])
## [1] 2.107162
(GBM_Tune_Rsquared <- GBM_Tune$results[GBM_Tune$results$shrinkage==GBM_Tune$bestTune$shrinkage &
                              GBM_Tune$results$interaction.depth==GBM_Tune$bestTune$interaction.depth &
                              GBM_Tune$results$n.minobsinnode==GBM_Tune$bestTune$n.minobsinnode &
                              GBM_Tune$results$n.trees==GBM_Tune$bestTune$n.trees,
                 c("Rsquared")])
## [1] 0.9285203
(GBM_Tune_MAE <- GBM_Tune$results[GBM_Tune$results$shrinkage==GBM_Tune$bestTune$shrinkage &
                              GBM_Tune$results$interaction.depth==GBM_Tune$bestTune$interaction.depth &
                              GBM_Tune$results$n.minobsinnode==GBM_Tune$bestTune$n.minobsinnode &
                              GBM_Tune$results$n.trees==GBM_Tune$bestTune$n.trees,
                 c("MAE")])
## [1] 1.559041

1.3.6.2 Random Forest (RF)


Code Chunk | Output
##################################
# Defining the model hyperparameter values
# for the RF model
##################################
RF_Grid = data.frame(mtry = c(100, 200, 300, 400, 500,
                              600, 700, 800, 900, 1000))

##################################
# Running the RF model
# by setting the caret method to 'RF'
##################################
set.seed(12345678)
RF_Tune <- train(x = MD.Model.Predictors,
                 y = MD$LIFEXP,
                 method = "rf",
                 tuneGrid = RF_Grid,
                 trControl = KFold_Control)

##################################
# Reporting the apparent results
# for the RF model
##################################
RF_DALEX <- DALEX::explain(RF_Tune,
                           data = MD.Model.Predictors,
                           y = MD$LIFEXP,
                           verbose = FALSE,
                           label = "RF")

(RF_DALEX_Performance <- model_performance(RF_DALEX))
## Measures for:  regression
## mse        : 0.9404626 
## rmse       : 0.9697745 
## r2         : 0.9848026 
## mad        : 0.493929
## 
## Residuals:
##          0%         10%         20%         30%         40%         50% 
## -3.89311943 -1.04739946 -0.63797813 -0.37774333 -0.17489179 -0.02355452 
##         60%         70%         80%         90%        100% 
##  0.11686047  0.37368288  0.59428965  1.20572149  3.83678360
(RF_DALEX_Diagnostics <- model_diagnostics(RF_DALEX))
##     GENDER              CONTIN       INFMOR           PERCAP       
##  Male  :139   Africa       :89   Min.   :0.3365   Min.   :-1.4775  
##  Female:153   Asia         :73   1st Qu.:1.7047   1st Qu.: 0.6183  
##               Europe       :62   Median :2.6100   Median : 1.7960  
##               North America:31   Mean   :2.5569   Mean   : 1.7571  
##               Oceania      :18   3rd Qu.:3.5025   3rd Qu.: 2.7938  
##               South America:19   Max.   :4.4864   Max.   : 4.7293  
##      CLTECH           NCOMOR            y             y_hat      
##  Min.   :  0.00   Min.   :1.866   Min.   :52.84   Min.   :54.37  
##  1st Qu.: 24.35   1st Qu.:3.944   1st Qu.:66.93   1st Qu.:67.15  
##  Median : 82.80   Median :4.717   Median :73.53   Median :73.58  
##  Mean   : 64.60   Mean   :4.652   Mean   :72.47   Mean   :72.49  
##  3rd Qu.:100.00   3rd Qu.:5.347   3rd Qu.:78.54   3rd Qu.:78.49  
##  Max.   :100.00   Max.   :7.959   Max.   :87.45   Max.   :86.25  
##    residuals        abs_residuals         label                ids        
##  Min.   :-3.89312   Min.   :0.004567   Length:292         Min.   :  1.00  
##  1st Qu.:-0.49373   1st Qu.:0.228644   Class :character   1st Qu.: 73.75  
##  Median :-0.02355   Median :0.493929   Mode  :character   Median :146.50  
##  Mean   :-0.01104   Mean   :0.695743                      Mean   :146.50  
##  3rd Qu.: 0.49410   3rd Qu.:0.996858                      3rd Qu.:219.25  
##  Max.   : 3.83678   Max.   :3.893119                      Max.   :292.00
plot(RF_DALEX_Diagnostics,
     variable = "y",
     yvariable = "y_hat") +
  geom_point(size=3) +
  scale_x_continuous("Observed LIFEXP") +
  scale_y_continuous("Predicted LIFEXP") +
  geom_abline(slope = 1) +
  ggtitle("RF: Observed and Predicted LIFEXP")

RF_DALEX_VariableImportance    <- model_parts(RF_DALEX,
                                              loss_function = loss_root_mean_square,
                                              B = 200,
                                              N = NULL)

plot(RF_DALEX_VariableImportance)

##################################
# Reporting the cross-validation results
# for the RF model
##################################
RF_Tune
## Random Forest 
## 
## 292 samples
##   6 predictor
## 
## No pre-processing
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 264, 263, 263, 262, 263, 264, ... 
## Resampling results across tuning parameters:
## 
##   mtry  RMSE      Rsquared   MAE     
##    100  2.244547  0.9196737  1.619943
##    200  2.249565  0.9196008  1.620459
##    300  2.251214  0.9194065  1.628754
##    400  2.239011  0.9201778  1.618476
##    500  2.244594  0.9199741  1.623557
##    600  2.250218  0.9195438  1.627748
##    700  2.250948  0.9195926  1.627966
##    800  2.252804  0.9194439  1.626298
##    900  2.256902  0.9191818  1.630857
##   1000  2.245525  0.9198362  1.618343
## 
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was mtry = 400.
RF_Tune$finalModel
## 
## Call:
##  randomForest(x = x, y = y, mtry = param$mtry) 
##                Type of random forest: regression
##                      Number of trees: 500
## No. of variables tried at each split: 6
## 
##           Mean of squared residuals: 5.126832
##                     % Var explained: 91.72
(RF_Tune_RMSE <- RF_Tune$results[RF_Tune$results$mtry==RF_Tune$bestTune$mtry,
                 c("RMSE")])
## [1] 2.239011
(RF_Tune_Rsquared <- RF_Tune$results[RF_Tune$results$mtry==RF_Tune$bestTune$mtry,
                 c("Rsquared")])
## [1] 0.9201778
(RF_Tune_MAE <- RF_Tune$results[RF_Tune$results$mtry==RF_Tune$bestTune$mtry,
                 c("MAE")])
## [1] 1.618476

1.3.6.3 Neural Network (NN)


Code Chunk | Output
##################################
# Defining the model hyperparameter values
# for the NN model
##################################
NN_Grid = expand.grid(size = c(2, 5, 10, 15, 20),
                      decay = c(0, 0.1, 0.001, 0.0001, 0.00001))

##################################
# Running the NN model
# by setting the caret method to 'NN'
##################################
set.seed(12345678)
NN_Tune <- train(x = MD.Model.Predictors,
                 y = MD$LIFEXP,
                 method = "nnet",
                 linout = TRUE,
                 preProcess = c('center', 'scale'),
                 maxit = 500,
                 tuneGrid = NN_Grid,
                 trControl = KFold_Control)
## # weights:  25
## initial  value 1400339.778929 
## iter  10 value 68496.910319
## iter  20 value 5956.174709
## iter  30 value 5844.262957
## iter  40 value 5352.739998
## iter  50 value 5259.580299
## iter  60 value 5258.019066
## iter  70 value 5205.090145
## iter  80 value 5196.195489
## iter  90 value 4833.831031
## iter 100 value 4569.510587
## iter 110 value 4553.721628
## iter 120 value 4553.045122
## iter 130 value 4553.015144
## iter 140 value 4552.876106
## iter 150 value 4550.489828
## iter 160 value 4550.208091
## iter 170 value 4550.152984
## iter 180 value 4550.031282
## iter 190 value 4549.764089
## iter 200 value 4549.444274
## iter 210 value 4548.348059
## iter 220 value 4548.157434
## iter 220 value 4548.157430
## iter 220 value 4548.157430
## final  value 4548.157430 
## converged
## # weights:  61
## initial  value 1419402.900363 
## iter  10 value 5466.160778
## iter  20 value 4136.215442
## iter  30 value 3233.377310
## iter  40 value 2184.748970
## iter  50 value 1482.694097
## iter  60 value 1341.742361
## iter  70 value 1297.232211
## iter  80 value 1213.738131
## iter  90 value 1146.376381
## iter 100 value 1070.241735
## iter 110 value 1030.232654
## iter 120 value 1015.217766
## iter 130 value 1005.639574
## iter 140 value 998.773821
## iter 150 value 992.324116
## iter 160 value 980.531489
## iter 170 value 978.495782
## iter 180 value 978.409744
## iter 190 value 978.361243
## iter 200 value 978.318025
## iter 210 value 978.296926
## iter 220 value 968.807011
## iter 230 value 939.853517
## iter 240 value 925.037978
## iter 250 value 919.646061
## iter 260 value 919.163802
## iter 270 value 917.547708
## iter 280 value 914.888136
## iter 290 value 911.397634
## iter 300 value 909.574590
## iter 310 value 908.742002
## iter 320 value 908.217371
## iter 330 value 907.307508
## iter 340 value 906.728904
## iter 350 value 906.413133
## iter 360 value 906.376723
## iter 370 value 906.329715
## iter 380 value 906.279582
## iter 390 value 906.271020
## iter 400 value 906.268970
## final  value 906.268949 
## converged
## # weights:  121
## initial  value 1369973.913442 
## iter  10 value 1456.280282
## iter  20 value 981.019262
## iter  30 value 820.891644
## iter  40 value 691.064010
## iter  50 value 615.847093
## iter  60 value 552.618476
## iter  70 value 501.933326
## iter  80 value 465.287566
## iter  90 value 435.639678
## iter 100 value 411.632837
## iter 110 value 377.319124
## iter 120 value 355.295277
## iter 130 value 332.770009
## iter 140 value 319.998174
## iter 150 value 311.670112
## iter 160 value 296.995967
## iter 170 value 289.673758
## iter 180 value 286.817395
## iter 190 value 285.327350
## iter 200 value 283.822117
## iter 210 value 281.823769
## iter 220 value 280.604183
## iter 230 value 279.960985
## iter 240 value 279.298747
## iter 250 value 278.767676
## iter 260 value 278.387227
## iter 270 value 277.570900
## iter 280 value 276.221841
## iter 290 value 273.862125
## iter 300 value 271.110331
## iter 310 value 269.686949
## iter 320 value 268.329167
## iter 330 value 266.353429
## iter 340 value 263.244321
## iter 350 value 262.025670
## iter 360 value 260.468974
## iter 370 value 259.082188
## iter 380 value 258.024935
## iter 390 value 257.709959
## iter 400 value 257.192116
## iter 410 value 256.946798
## iter 420 value 256.805007
## iter 430 value 256.770626
## iter 440 value 256.766595
## iter 450 value 256.763944
## iter 460 value 256.762805
## iter 470 value 256.759951
## iter 480 value 256.742706
## final  value 256.731685 
## converged
## # weights:  181
## initial  value 1369859.779729 
## iter  10 value 1218.791825
## iter  20 value 844.152299
## iter  30 value 679.366662
## iter  40 value 572.273305
## iter  50 value 466.657882
## iter  60 value 383.138573
## iter  70 value 339.951431
## iter  80 value 283.173217
## iter  90 value 257.527205
## iter 100 value 237.333601
## iter 110 value 223.739667
## iter 120 value 215.169024
## iter 130 value 208.082918
## iter 140 value 199.329810
## iter 150 value 191.342838
## iter 160 value 184.771082
## iter 170 value 174.014610
## iter 180 value 167.789067
## iter 190 value 161.398029
## iter 200 value 156.888476
## iter 210 value 153.435980
## iter 220 value 149.579761
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## iter 250 value 140.214188
## iter 260 value 137.517961
## iter 270 value 135.175708
## iter 280 value 132.482019
## iter 290 value 129.935560
## iter 300 value 127.301305
## iter 310 value 124.632966
## iter 320 value 122.671009
## iter 330 value 119.063976
## iter 340 value 115.950398
## iter 350 value 113.321558
## iter 360 value 110.553146
## iter 370 value 109.537855
## iter 380 value 108.980326
## iter 390 value 108.142979
## iter 400 value 107.033356
## iter 410 value 106.482910
## iter 420 value 105.642504
## iter 430 value 104.684540
## iter 440 value 103.914639
## iter 450 value 102.861627
## iter 460 value 101.598500
## iter 470 value 99.744133
## iter 480 value 97.351172
## iter 490 value 95.563516
## iter 500 value 93.616614
## final  value 93.616614 
## stopped after 500 iterations
## # weights:  241
## initial  value 1362136.072048 
## iter  10 value 1398.158403
## iter  20 value 813.986040
## iter  30 value 623.314450
## iter  40 value 451.572085
## iter  50 value 343.423349
## iter  60 value 299.481028
## iter  70 value 258.539324
## iter  80 value 218.722855
## iter  90 value 189.014309
## iter 100 value 172.421623
## iter 110 value 152.085135
## iter 120 value 140.177393
## iter 130 value 130.443034
## iter 140 value 121.601687
## iter 150 value 114.523579
## iter 160 value 107.718749
## iter 170 value 97.564388
## iter 180 value 92.637160
## iter 190 value 87.632255
## iter 200 value 83.334630
## iter 210 value 78.936737
## iter 220 value 74.404872
## iter 230 value 71.245308
## iter 240 value 69.350806
## iter 250 value 66.556843
## iter 260 value 63.476653
## iter 270 value 61.090865
## iter 280 value 59.602674
## iter 290 value 58.022193
## iter 300 value 55.931747
## iter 310 value 54.279973
## iter 320 value 52.788004
## iter 330 value 52.144060
## iter 340 value 51.520501
## iter 350 value 50.937822
## iter 360 value 50.600355
## iter 370 value 50.390622
## iter 380 value 50.202609
## iter 390 value 49.915583
## iter 400 value 49.675294
## iter 410 value 49.500596
## iter 420 value 49.385656
## iter 430 value 49.276835
## iter 440 value 49.181953
## iter 450 value 49.072194
## iter 460 value 48.923516
## iter 470 value 48.704723
## iter 480 value 48.485576
## iter 490 value 48.352389
## iter 500 value 48.312069
## final  value 48.312069 
## stopped after 500 iterations
## # weights:  25
## initial  value 1384018.650104 
## iter  10 value 15568.108680
## iter  20 value 7078.471518
## iter  30 value 5823.550517
## iter  40 value 4681.910341
## iter  50 value 3892.420056
## iter  60 value 2611.064130
## iter  70 value 2192.772551
## iter  80 value 2005.636016
## iter  90 value 1807.236078
## iter 100 value 1636.352081
## iter 110 value 1526.444834
## iter 120 value 1483.669235
## iter 130 value 1476.459207
## iter 140 value 1448.987229
## iter 150 value 1442.620774
## final  value 1442.569494 
## converged
## # weights:  61
## initial  value 1394098.585171 
## iter  10 value 26536.454537
## iter  20 value 11933.656186
## iter  30 value 8409.704475
## iter  40 value 7714.766464
## iter  50 value 6574.341375
## iter  60 value 4426.666175
## iter  70 value 3181.854615
## iter  80 value 2165.503641
## iter  90 value 1589.793031
## iter 100 value 1395.847733
## iter 110 value 1303.531922
## iter 120 value 1202.196454
## iter 130 value 1137.112382
## iter 140 value 1094.154891
## iter 150 value 1051.603814
## iter 160 value 1040.897230
## iter 170 value 1024.371791
## iter 180 value 998.908996
## iter 190 value 980.361705
## iter 200 value 968.920478
## iter 210 value 960.269924
## iter 220 value 958.949560
## iter 230 value 955.625569
## iter 240 value 954.622306
## iter 250 value 954.575117
## iter 260 value 954.545205
## final  value 954.529372 
## converged
## # weights:  121
## initial  value 1387428.049031 
## iter  10 value 1450.061966
## iter  20 value 965.028030
## iter  30 value 832.187617
## iter  40 value 742.640384
## iter  50 value 706.951827
## iter  60 value 671.165099
## iter  70 value 655.198531
## iter  80 value 640.955382
## iter  90 value 631.411994
## iter 100 value 623.066768
## iter 110 value 616.700789
## iter 120 value 608.599648
## iter 130 value 600.341217
## iter 140 value 592.731460
## iter 150 value 588.543437
## iter 160 value 584.942937
## iter 170 value 579.711382
## iter 180 value 574.896506
## iter 190 value 570.791378
## iter 200 value 565.916376
## iter 210 value 561.811951
## iter 220 value 558.347273
## iter 230 value 555.115952
## iter 240 value 552.601403
## iter 250 value 551.234121
## iter 260 value 549.812405
## iter 270 value 545.883743
## iter 280 value 543.309314
## iter 290 value 540.965899
## iter 300 value 539.338412
## iter 310 value 537.312268
## iter 320 value 534.414925
## iter 330 value 532.313464
## iter 340 value 530.388935
## iter 350 value 528.555728
## iter 360 value 527.856224
## iter 370 value 527.200364
## iter 380 value 526.704580
## iter 390 value 526.585428
## iter 400 value 526.560876
## iter 410 value 526.557217
## iter 420 value 526.556435
## iter 430 value 526.556285
## final  value 526.556224 
## converged
## # weights:  181
## initial  value 1353091.485763 
## iter  10 value 1287.823903
## iter  20 value 938.702167
## iter  30 value 748.096569
## iter  40 value 639.041680
## iter  50 value 556.282172
## iter  60 value 513.004807
## iter  70 value 483.894119
## iter  80 value 465.535296
## iter  90 value 449.084974
## iter 100 value 433.926647
## iter 110 value 425.401047
## iter 120 value 418.452940
## iter 130 value 414.162494
## iter 140 value 408.433218
## iter 150 value 404.732510
## iter 160 value 400.845451
## iter 170 value 397.982738
## iter 180 value 396.561282
## iter 190 value 395.014302
## iter 200 value 393.463659
## iter 210 value 391.791284
## iter 220 value 388.330655
## iter 230 value 386.385810
## iter 240 value 385.284808
## iter 250 value 383.783272
## iter 260 value 382.369491
## iter 270 value 381.739305
## iter 280 value 381.211069
## iter 290 value 379.185604
## iter 300 value 376.938740
## iter 310 value 376.298644
## iter 320 value 375.985631
## iter 330 value 375.767452
## iter 340 value 375.640929
## iter 350 value 375.399286
## iter 360 value 374.985965
## iter 370 value 374.872178
## iter 380 value 374.823068
## iter 390 value 374.719717
## iter 400 value 374.630241
## iter 410 value 374.580365
## iter 420 value 374.540869
## iter 430 value 374.492645
## iter 440 value 374.432551
## iter 450 value 374.330813
## iter 460 value 374.220902
## iter 470 value 374.157806
## iter 480 value 374.022897
## iter 490 value 373.172452
## iter 500 value 372.719725
## final  value 372.719725 
## stopped after 500 iterations
## # weights:  241
## initial  value 1379427.806099 
## iter  10 value 1318.482661
## iter  20 value 948.464684
## iter  30 value 781.314962
## iter  40 value 694.439588
## iter  50 value 613.809462
## iter  60 value 546.586849
## iter  70 value 499.266011
## iter  80 value 473.301240
## iter  90 value 453.074641
## iter 100 value 434.018863
## iter 110 value 422.579540
## iter 120 value 415.664194
## iter 130 value 409.582708
## iter 140 value 404.703083
## iter 150 value 400.088954
## iter 160 value 396.840796
## iter 170 value 393.550983
## iter 180 value 389.392143
## iter 190 value 384.520420
## iter 200 value 380.059544
## iter 210 value 376.737395
## iter 220 value 373.795749
## iter 230 value 371.647825
## iter 240 value 369.437417
## iter 250 value 367.198938
## iter 260 value 365.469468
## iter 270 value 363.797594
## iter 280 value 361.902571
## iter 290 value 360.302742
## iter 300 value 358.834282
## iter 310 value 357.653361
## iter 320 value 356.220173
## iter 330 value 354.742659
## iter 340 value 353.187152
## iter 350 value 352.317298
## iter 360 value 351.597133
## iter 370 value 350.748670
## iter 380 value 350.035629
## iter 390 value 349.251348
## iter 400 value 348.627508
## iter 410 value 348.035506
## iter 420 value 347.554107
## iter 430 value 347.055057
## iter 440 value 346.506821
## iter 450 value 346.028052
## iter 460 value 345.701868
## iter 470 value 345.448568
## iter 480 value 345.273019
## iter 490 value 345.171795
## iter 500 value 345.004260
## final  value 345.004260 
## stopped after 500 iterations
## # weights:  25
## initial  value 1423891.582406 
## iter  10 value 12537.489344
## iter  20 value 8269.674014
## iter  30 value 5421.520107
## iter  40 value 1535.106046
## iter  50 value 1444.459711
## iter  60 value 1406.797260
## iter  70 value 1230.493759
## iter  80 value 1084.242081
## iter  90 value 960.172332
## iter 100 value 943.333703
## iter 110 value 942.305098
## iter 120 value 930.335846
## iter 130 value 929.577196
## iter 140 value 928.927542
## iter 150 value 928.888316
## iter 160 value 928.401816
## iter 170 value 928.384803
## iter 180 value 926.787741
## iter 190 value 925.593135
## iter 200 value 925.551762
## iter 210 value 925.462572
## iter 220 value 924.972394
## iter 230 value 924.793673
## iter 240 value 921.986206
## iter 250 value 921.375885
## iter 260 value 921.237857
## iter 270 value 921.223130
## iter 280 value 921.220621
## iter 290 value 921.154089
## iter 300 value 921.110078
## iter 310 value 921.097205
## final  value 921.097031 
## converged
## # weights:  61
## initial  value 1412917.868612 
## iter  10 value 47303.215826
## iter  20 value 11772.187478
## iter  30 value 9642.666220
## iter  40 value 7039.730221
## iter  50 value 3438.511113
## iter  60 value 2153.777110
## iter  70 value 1457.745486
## iter  80 value 1089.836911
## iter  90 value 980.416563
## iter 100 value 933.168147
## iter 110 value 889.400530
## iter 120 value 870.160480
## iter 130 value 864.401688
## iter 140 value 857.626325
## iter 150 value 850.453945
## iter 160 value 841.635431
## iter 170 value 821.291746
## iter 180 value 805.288555
## iter 190 value 800.439409
## iter 200 value 793.712157
## iter 210 value 782.194383
## iter 220 value 776.109391
## iter 230 value 765.630829
## iter 240 value 753.827264
## iter 250 value 750.095525
## iter 260 value 747.055166
## iter 270 value 744.176972
## iter 280 value 738.447826
## iter 290 value 730.003868
## iter 300 value 716.537518
## iter 310 value 715.365196
## iter 320 value 713.454410
## iter 330 value 711.280430
## iter 340 value 709.210457
## iter 350 value 707.570229
## iter 360 value 706.449745
## iter 370 value 705.894708
## iter 380 value 705.207222
## iter 390 value 705.024693
## iter 400 value 704.997233
## iter 410 value 704.995416
## iter 420 value 704.994876
## iter 420 value 704.994873
## iter 420 value 704.994873
## final  value 704.994873 
## converged
## # weights:  121
## initial  value 1433584.753726 
## iter  10 value 2356.920606
## iter  20 value 1146.044953
## iter  30 value 894.843159
## iter  40 value 748.909966
## iter  50 value 659.434098
## iter  60 value 583.222552
## iter  70 value 521.519816
## iter  80 value 490.932249
## iter  90 value 466.770159
## iter 100 value 449.518369
## iter 110 value 421.297782
## iter 120 value 396.615299
## iter 130 value 383.763143
## iter 140 value 376.869871
## iter 150 value 365.351570
## iter 160 value 356.752188
## iter 170 value 347.514557
## iter 180 value 339.875712
## iter 190 value 334.620596
## iter 200 value 327.643579
## iter 210 value 324.005633
## iter 220 value 321.439653
## iter 230 value 318.718763
## iter 240 value 316.720846
## iter 250 value 316.349474
## iter 260 value 316.258847
## iter 270 value 316.004443
## iter 280 value 315.429764
## iter 290 value 314.608690
## iter 300 value 314.017114
## iter 310 value 313.057631
## iter 320 value 311.650614
## iter 330 value 303.149669
## iter 340 value 292.307685
## iter 350 value 283.880966
## iter 360 value 278.707246
## iter 370 value 275.872878
## iter 380 value 274.003154
## iter 390 value 272.594295
## iter 400 value 271.087957
## iter 410 value 269.693184
## iter 420 value 268.877864
## iter 430 value 267.877948
## iter 440 value 267.495669
## iter 450 value 267.155985
## iter 460 value 266.930277
## iter 470 value 266.848237
## iter 480 value 266.813716
## iter 490 value 266.803428
## iter 500 value 266.802076
## final  value 266.802076 
## stopped after 500 iterations
## # weights:  181
## initial  value 1394930.794764 
## iter  10 value 1279.269553
## iter  20 value 868.323818
## iter  30 value 719.556231
## iter  40 value 561.315354
## iter  50 value 464.560336
## iter  60 value 412.004354
## iter  70 value 373.089421
## iter  80 value 327.225673
## iter  90 value 296.791422
## iter 100 value 275.365839
## iter 110 value 258.632874
## iter 120 value 247.519543
## iter 130 value 238.913534
## iter 140 value 224.320611
## iter 150 value 211.431281
## iter 160 value 195.649973
## iter 170 value 184.992278
## iter 180 value 179.439211
## iter 190 value 174.741821
## iter 200 value 171.307787
## iter 210 value 167.550830
## iter 220 value 164.440970
## iter 230 value 162.559312
## iter 240 value 159.905158
## iter 250 value 158.674720
## iter 260 value 157.792217
## iter 270 value 156.438216
## iter 280 value 154.814089
## iter 290 value 153.285061
## iter 300 value 152.266027
## iter 310 value 151.416318
## iter 320 value 150.592960
## iter 330 value 149.935923
## iter 340 value 149.200792
## iter 350 value 148.616745
## iter 360 value 148.272016
## iter 370 value 148.148050
## iter 380 value 148.073069
## iter 390 value 147.871002
## iter 400 value 147.648666
## iter 410 value 147.288195
## iter 420 value 146.711104
## iter 430 value 145.937980
## iter 440 value 145.451685
## iter 450 value 144.939674
## iter 460 value 144.271039
## iter 470 value 143.147613
## iter 480 value 141.523488
## iter 490 value 139.291811
## iter 500 value 136.917199
## final  value 136.917199 
## stopped after 500 iterations
## # weights:  241
## initial  value 1415447.001248 
## iter  10 value 1560.997067
## iter  20 value 900.702298
## iter  30 value 647.249066
## iter  40 value 492.717841
## iter  50 value 405.391326
## iter  60 value 345.438590
## iter  70 value 306.750507
## iter  80 value 267.845476
## iter  90 value 235.799128
## iter 100 value 203.677835
## iter 110 value 183.091395
## iter 120 value 166.683630
## iter 130 value 150.230650
## iter 140 value 138.325315
## iter 150 value 126.601246
## iter 160 value 117.089372
## iter 170 value 109.712789
## iter 180 value 104.980911
## iter 190 value 100.729998
## iter 200 value 97.313505
## iter 210 value 94.693012
## iter 220 value 91.855427
## iter 230 value 88.698395
## iter 240 value 86.415252
## iter 250 value 84.194762
## iter 260 value 81.902251
## iter 270 value 79.161843
## iter 280 value 76.602614
## iter 290 value 74.852013
## iter 300 value 72.364602
## iter 310 value 70.525242
## iter 320 value 69.077758
## iter 330 value 67.575295
## iter 340 value 66.004828
## iter 350 value 64.995929
## iter 360 value 64.086367
## iter 370 value 63.280264
## iter 380 value 62.223884
## iter 390 value 61.269493
## iter 400 value 60.774563
## iter 410 value 59.812007
## iter 420 value 58.774771
## iter 430 value 58.390361
## iter 440 value 58.186240
## iter 450 value 58.035690
## iter 460 value 57.846932
## iter 470 value 57.675788
## iter 480 value 57.473200
## iter 490 value 57.338265
## iter 500 value 57.308874
## final  value 57.308874 
## stopped after 500 iterations
## # weights:  25
## initial  value 1386870.681414 
## iter  10 value 16073.496682
## iter  20 value 15376.477898
## iter  30 value 11345.105417
## iter  40 value 10198.597925
## iter  50 value 6428.790073
## iter  60 value 5644.346354
## iter  70 value 5497.965330
## iter  80 value 5421.132636
## iter  90 value 5179.625122
## iter 100 value 4884.074564
## iter 110 value 4234.055230
## iter 120 value 2904.598017
## iter 130 value 1556.481872
## iter 140 value 1388.490514
## iter 150 value 1364.959883
## iter 160 value 1354.867166
## iter 170 value 1328.410308
## iter 180 value 1319.516961
## iter 190 value 1314.239222
## iter 200 value 1311.288587
## iter 210 value 1309.937188
## iter 220 value 1309.893532
## iter 230 value 1307.847786
## iter 240 value 1294.078490
## iter 250 value 1283.816175
## iter 260 value 1278.919102
## iter 270 value 1270.853058
## iter 280 value 1258.307996
## iter 290 value 1252.139480
## iter 300 value 1232.788080
## iter 310 value 1199.651938
## iter 320 value 1172.256003
## iter 330 value 1166.258096
## iter 340 value 1163.088009
## iter 350 value 1162.089573
## iter 360 value 1160.698645
## iter 370 value 1133.349758
## iter 380 value 1079.030491
## iter 390 value 1074.131190
## iter 400 value 1073.353709
## iter 410 value 1072.970806
## iter 420 value 1070.957063
## iter 430 value 1068.827441
## iter 440 value 1068.618943
## final  value 1068.618146 
## converged
## # weights:  61
## initial  value 1369944.309207 
## iter  10 value 4813.258129
## iter  20 value 2647.028707
## iter  30 value 2018.882759
## iter  40 value 1448.844785
## iter  50 value 1310.679541
## iter  60 value 1237.240533
## iter  70 value 1144.330366
## iter  80 value 930.396868
## iter  90 value 844.047470
## iter 100 value 813.793452
## iter 110 value 799.122112
## iter 120 value 791.694476
## iter 130 value 785.215152
## iter 140 value 780.425639
## iter 150 value 773.020497
## iter 160 value 768.206613
## iter 170 value 755.018782
## iter 180 value 753.053456
## iter 190 value 749.706268
## iter 200 value 748.552178
## iter 210 value 741.293601
## iter 220 value 722.928566
## iter 230 value 718.856657
## iter 240 value 711.482516
## iter 250 value 703.614285
## iter 260 value 688.171332
## iter 270 value 686.786797
## iter 280 value 686.583852
## iter 290 value 686.442883
## iter 300 value 686.361656
## iter 310 value 684.560410
## iter 320 value 676.478486
## iter 330 value 675.804383
## iter 340 value 675.380648
## iter 350 value 674.316048
## iter 360 value 673.271927
## iter 370 value 672.798969
## iter 380 value 672.329418
## iter 390 value 672.184275
## iter 400 value 672.069031
## iter 410 value 671.911016
## iter 420 value 671.861759
## iter 430 value 671.662889
## iter 440 value 671.563037
## iter 450 value 671.496963
## iter 460 value 671.492484
## iter 470 value 671.488292
## iter 480 value 671.485325
## iter 490 value 671.482358
## iter 490 value 671.482352
## iter 500 value 671.480801
## final  value 671.480801 
## stopped after 500 iterations
## # weights:  121
## initial  value 1448634.114928 
## iter  10 value 13322.704711
## iter  20 value 4075.345309
## iter  30 value 3080.568831
## iter  40 value 2774.136853
## iter  50 value 2362.916182
## iter  60 value 2080.977179
## iter  70 value 1883.607992
## iter  80 value 1479.718416
## iter  90 value 1357.647737
## iter 100 value 1166.263645
## iter 110 value 1038.656966
## iter 120 value 910.916558
## iter 130 value 871.834875
## iter 140 value 845.580955
## iter 150 value 836.637035
## iter 160 value 830.920086
## iter 170 value 822.754485
## iter 180 value 807.472361
## iter 190 value 798.551929
## iter 200 value 796.890960
## iter 210 value 789.559091
## iter 220 value 768.048484
## iter 230 value 760.768959
## iter 240 value 750.867893
## iter 250 value 747.409155
## iter 260 value 746.976263
## iter 270 value 737.868555
## iter 280 value 721.564300
## iter 290 value 717.783889
## iter 300 value 714.155859
## iter 310 value 714.000603
## iter 320 value 709.916810
## iter 330 value 702.465299
## iter 340 value 696.906456
## iter 350 value 690.620258
## iter 360 value 681.004312
## iter 370 value 672.140070
## iter 380 value 667.202449
## iter 390 value 657.951654
## iter 400 value 636.257278
## iter 410 value 629.163597
## iter 420 value 626.709340
## iter 430 value 624.262775
## iter 440 value 616.050756
## iter 450 value 604.893254
## iter 460 value 600.444007
## iter 470 value 600.302006
## iter 480 value 600.114035
## iter 490 value 595.541228
## iter 500 value 585.092013
## final  value 585.092013 
## stopped after 500 iterations
## # weights:  181
## initial  value 1413917.762241 
## iter  10 value 1208.414670
## iter  20 value 780.378653
## iter  30 value 659.898262
## iter  40 value 534.589345
## iter  50 value 438.795352
## iter  60 value 383.367617
## iter  70 value 353.562699
## iter  80 value 308.163367
## iter  90 value 285.129490
## iter 100 value 269.971590
## iter 110 value 256.359031
## iter 120 value 246.275217
## iter 130 value 240.603830
## iter 140 value 234.079948
## iter 150 value 222.710452
## iter 160 value 215.770746
## iter 170 value 208.990527
## iter 180 value 204.697435
## iter 190 value 200.427219
## iter 200 value 197.721322
## iter 210 value 194.155305
## iter 220 value 191.774604
## iter 230 value 189.688585
## iter 240 value 188.116083
## iter 250 value 186.456308
## iter 260 value 184.470036
## iter 270 value 182.670852
## iter 280 value 179.775918
## iter 290 value 176.698129
## iter 300 value 173.555253
## iter 310 value 171.106637
## iter 320 value 169.713848
## iter 330 value 168.716446
## iter 340 value 167.417298
## iter 350 value 166.109809
## iter 360 value 164.690294
## iter 370 value 163.663174
## iter 380 value 163.111452
## iter 390 value 162.145033
## iter 400 value 160.248573
## iter 410 value 159.111763
## iter 420 value 157.290832
## iter 430 value 155.851294
## iter 440 value 154.377538
## iter 450 value 152.744293
## iter 460 value 151.144088
## iter 470 value 150.015990
## iter 480 value 148.359773
## iter 490 value 145.605921
## iter 500 value 142.279220
## final  value 142.279220 
## stopped after 500 iterations
## # weights:  241
## initial  value 1408462.442303 
## iter  10 value 1696.574721
## iter  20 value 834.666584
## iter  30 value 643.461071
## iter  40 value 499.893272
## iter  50 value 396.200813
## iter  60 value 352.163182
## iter  70 value 294.997125
## iter  80 value 262.226205
## iter  90 value 232.559809
## iter 100 value 213.077272
## iter 110 value 192.442938
## iter 120 value 175.768022
## iter 130 value 161.839371
## iter 140 value 152.054105
## iter 150 value 144.414445
## iter 160 value 135.492289
## iter 170 value 125.738463
## iter 180 value 118.036333
## iter 190 value 110.490285
## iter 200 value 104.562541
## iter 210 value 99.692701
## iter 220 value 94.861440
## iter 230 value 90.734906
## iter 240 value 87.690553
## iter 250 value 84.938820
## iter 260 value 82.629609
## iter 270 value 78.976927
## iter 280 value 76.655006
## iter 290 value 74.724031
## iter 300 value 70.914051
## iter 310 value 66.237798
## iter 320 value 63.612419
## iter 330 value 61.827949
## iter 340 value 59.937911
## iter 350 value 58.045567
## iter 360 value 56.209759
## iter 370 value 54.309045
## iter 380 value 53.266239
## iter 390 value 52.358559
## iter 400 value 51.581962
## iter 410 value 50.605355
## iter 420 value 49.724843
## iter 430 value 48.713986
## iter 440 value 47.803062
## iter 450 value 47.197608
## iter 460 value 46.564369
## iter 470 value 46.010227
## iter 480 value 45.543231
## iter 490 value 45.250383
## iter 500 value 45.190447
## final  value 45.190447 
## stopped after 500 iterations
## # weights:  25
## initial  value 1372476.830600 
## iter  10 value 66071.850729
## iter  20 value 16301.884162
## iter  30 value 15924.138647
## iter  40 value 15669.361705
## iter  50 value 15504.252382
## iter  60 value 14128.677558
## iter  70 value 12415.066884
## iter  80 value 11731.682723
## iter  90 value 11597.027862
## iter 100 value 11542.573502
## iter 110 value 11516.507493
## final  value 11516.361465 
## converged
## # weights:  61
## initial  value 1407938.264648 
## iter  10 value 2843.758487
## iter  20 value 1843.940204
## iter  30 value 1205.152712
## iter  40 value 960.451207
## iter  50 value 866.733606
## iter  60 value 752.674082
## iter  70 value 726.720860
## iter  80 value 720.863376
## iter  90 value 717.945696
## iter 100 value 715.769818
## iter 110 value 691.123577
## iter 120 value 668.661251
## iter 130 value 663.267880
## iter 140 value 661.586601
## iter 150 value 659.652976
## iter 160 value 656.104034
## iter 170 value 647.580423
## iter 180 value 636.526074
## iter 190 value 620.068787
## iter 200 value 609.319300
## iter 210 value 601.334325
## iter 220 value 599.195914
## iter 230 value 598.074594
## iter 240 value 596.885061
## iter 250 value 595.768566
## iter 260 value 595.725856
## iter 270 value 595.583514
## iter 280 value 595.257327
## iter 290 value 595.038863
## iter 300 value 593.962638
## iter 310 value 592.675694
## iter 320 value 591.742046
## iter 330 value 591.226827
## iter 340 value 590.903815
## iter 350 value 590.603719
## iter 360 value 590.340362
## iter 370 value 590.029252
## iter 380 value 589.625512
## iter 390 value 589.568694
## iter 400 value 587.965804
## iter 410 value 585.626207
## iter 420 value 585.403580
## iter 430 value 585.329958
## iter 440 value 585.152390
## iter 450 value 584.868036
## iter 460 value 584.553890
## iter 470 value 584.402547
## iter 480 value 584.398097
## iter 490 value 584.299566
## iter 500 value 583.819417
## final  value 583.819417 
## stopped after 500 iterations
## # weights:  121
## initial  value 1400959.315384 
## iter  10 value 3680.038454
## iter  20 value 1607.147930
## iter  30 value 1149.990065
## iter  40 value 880.060931
## iter  50 value 765.302278
## iter  60 value 684.724229
## iter  70 value 645.722958
## iter  80 value 605.969592
## iter  90 value 572.317587
## iter 100 value 541.210496
## iter 110 value 532.617162
## iter 120 value 522.482997
## iter 130 value 499.551610
## iter 140 value 481.594549
## iter 150 value 471.460600
## iter 160 value 467.220832
## iter 170 value 464.111575
## iter 180 value 460.605383
## iter 190 value 457.742952
## iter 200 value 456.204903
## iter 210 value 454.861228
## iter 220 value 453.628710
## iter 230 value 452.713377
## iter 240 value 452.048616
## iter 250 value 451.688932
## iter 260 value 451.554390
## iter 270 value 451.302769
## iter 280 value 450.649808
## iter 290 value 449.418756
## iter 300 value 446.983399
## iter 310 value 445.765067
## iter 320 value 444.221886
## iter 330 value 442.906368
## iter 340 value 441.921796
## iter 350 value 439.230664
## iter 360 value 437.305346
## iter 370 value 436.151747
## iter 380 value 435.218985
## iter 390 value 434.474820
## iter 400 value 434.186149
## iter 410 value 433.971768
## iter 420 value 433.890355
## iter 430 value 433.850597
## iter 440 value 433.767619
## iter 450 value 433.686352
## iter 460 value 433.649166
## iter 470 value 433.595156
## iter 480 value 433.579809
## iter 490 value 433.568401
## iter 500 value 433.568055
## final  value 433.568055 
## stopped after 500 iterations
## # weights:  181
## initial  value 1436380.947420 
## iter  10 value 1396.649747
## iter  20 value 780.122467
## iter  30 value 624.376353
## iter  40 value 498.828115
## iter  50 value 436.726747
## iter  60 value 387.216554
## iter  70 value 353.334294
## iter  80 value 316.159909
## iter  90 value 293.793647
## iter 100 value 279.920857
## iter 110 value 270.886633
## iter 120 value 262.272272
## iter 130 value 255.845747
## iter 140 value 245.837874
## iter 150 value 238.644956
## iter 160 value 234.031603
## iter 170 value 230.873214
## iter 180 value 227.327736
## iter 190 value 222.393137
## iter 200 value 217.196154
## iter 210 value 213.413307
## iter 220 value 210.916850
## iter 230 value 208.859381
## iter 240 value 206.545490
## iter 250 value 205.318510
## iter 260 value 203.278624
## iter 270 value 201.512503
## iter 280 value 200.005246
## iter 290 value 198.385431
## iter 300 value 196.930479
## iter 310 value 196.159406
## iter 320 value 195.300880
## iter 330 value 194.737660
## iter 340 value 193.759869
## iter 350 value 192.962626
## iter 360 value 192.513935
## iter 370 value 192.116019
## iter 380 value 192.053505
## iter 390 value 191.973327
## iter 400 value 191.804354
## iter 410 value 191.624843
## iter 420 value 191.473645
## iter 430 value 191.157138
## iter 440 value 190.857355
## iter 450 value 190.521803
## iter 460 value 190.052830
## iter 470 value 189.696721
## iter 480 value 189.502272
## iter 490 value 189.216275
## iter 500 value 188.874484
## final  value 188.874484 
## stopped after 500 iterations
## # weights:  241
## initial  value 1361337.236566 
## iter  10 value 1219.972364
## iter  20 value 853.936547
## iter  30 value 638.681809
## iter  40 value 510.339447
## iter  50 value 429.476184
## iter  60 value 354.555508
## iter  70 value 294.870702
## iter  80 value 252.298186
## iter  90 value 217.298982
## iter 100 value 185.503750
## iter 110 value 170.873262
## iter 120 value 157.969799
## iter 130 value 146.805724
## iter 140 value 131.943708
## iter 150 value 117.770202
## iter 160 value 102.266261
## iter 170 value 85.934729
## iter 180 value 74.247126
## iter 190 value 68.101719
## iter 200 value 62.900901
## iter 210 value 59.702907
## iter 220 value 57.895174
## iter 230 value 56.258084
## iter 240 value 53.783025
## iter 250 value 51.804822
## iter 260 value 49.186864
## iter 270 value 47.302727
## iter 280 value 45.712787
## iter 290 value 44.955095
## iter 300 value 43.616174
## iter 310 value 42.655876
## iter 320 value 42.143912
## iter 330 value 41.577669
## iter 340 value 40.824645
## iter 350 value 40.438672
## iter 360 value 40.091822
## iter 370 value 39.797508
## iter 380 value 39.639969
## iter 390 value 39.538686
## iter 400 value 39.451142
## iter 410 value 39.303839
## iter 420 value 39.101274
## iter 430 value 38.545017
## iter 440 value 38.328375
## iter 450 value 38.109891
## iter 460 value 37.704041
## iter 470 value 37.506902
## iter 480 value 37.394181
## iter 490 value 37.358919
## iter 500 value 37.349176
## final  value 37.349176 
## stopped after 500 iterations
## # weights:  25
## initial  value 1387099.627384 
## iter  10 value 6884.863381
## iter  20 value 5742.708364
## iter  30 value 5648.924554
## iter  40 value 5489.348356
## iter  50 value 5294.227643
## iter  60 value 4191.619062
## iter  70 value 3439.820488
## iter  80 value 1865.159081
## iter  90 value 1354.174023
## iter 100 value 1230.275976
## iter 110 value 1213.606377
## iter 120 value 1203.457053
## iter 130 value 1172.423722
## iter 140 value 1157.156416
## iter 150 value 1149.554267
## iter 160 value 1148.385147
## iter 170 value 1145.328761
## iter 180 value 1133.782683
## iter 190 value 1115.050236
## iter 200 value 1098.741020
## iter 210 value 1071.941371
## iter 220 value 1069.565696
## iter 230 value 1068.972566
## iter 240 value 1065.824168
## iter 250 value 1063.384913
## iter 260 value 1061.492019
## iter 270 value 1061.043238
## iter 280 value 1061.026919
## iter 290 value 1060.383288
## iter 300 value 1059.598781
## iter 310 value 1058.796290
## iter 320 value 1058.548991
## iter 330 value 1058.546728
## iter 340 value 1058.471061
## iter 350 value 1057.953294
## iter 360 value 1057.525014
## iter 370 value 1057.265463
## iter 380 value 1057.261163
## iter 380 value 1057.261161
## iter 380 value 1057.261157
## final  value 1057.261157 
## converged
## # weights:  61
## initial  value 1409061.029459 
## iter  10 value 1454.501398
## iter  20 value 1173.662421
## iter  30 value 948.423994
## iter  40 value 818.321733
## iter  50 value 723.675688
## iter  60 value 669.952133
## iter  70 value 632.214352
## iter  80 value 621.930101
## iter  90 value 612.884284
## iter 100 value 610.410488
## iter 110 value 608.256095
## iter 120 value 607.334157
## iter 130 value 606.911674
## iter 140 value 606.777125
## iter 150 value 606.119864
## iter 160 value 604.317289
## iter 170 value 600.597374
## iter 180 value 593.442563
## iter 190 value 589.851119
## iter 200 value 587.664736
## iter 210 value 585.790198
## iter 220 value 584.125603
## iter 230 value 582.950025
## iter 240 value 582.386864
## iter 250 value 582.116854
## iter 260 value 582.086040
## iter 270 value 581.971393
## iter 280 value 581.690192
## iter 290 value 581.460932
## iter 300 value 581.353291
## iter 310 value 581.149949
## iter 320 value 580.848503
## iter 330 value 580.611379
## iter 340 value 580.393534
## iter 350 value 580.266394
## iter 360 value 580.141481
## iter 370 value 579.957066
## iter 380 value 579.953258
## iter 390 value 579.941049
## iter 400 value 579.880828
## iter 410 value 579.788238
## iter 420 value 579.685639
## iter 430 value 579.303637
## iter 440 value 579.152107
## iter 450 value 578.975600
## iter 460 value 578.868404
## iter 470 value 578.775307
## iter 480 value 578.687300
## iter 490 value 578.665848
## final  value 578.663563 
## converged
## # weights:  121
## initial  value 1416636.374166 
## iter  10 value 1671.478236
## iter  20 value 1000.629062
## iter  30 value 821.121997
## iter  40 value 713.814097
## iter  50 value 641.158097
## iter  60 value 608.680645
## iter  70 value 586.740869
## iter  80 value 567.069916
## iter  90 value 553.661699
## iter 100 value 545.983177
## iter 110 value 542.387548
## iter 120 value 538.795445
## iter 130 value 532.736078
## iter 140 value 527.976666
## iter 150 value 518.717146
## iter 160 value 509.550051
## iter 170 value 494.733760
## iter 180 value 487.147887
## iter 190 value 468.792136
## iter 200 value 454.176554
## iter 210 value 446.169427
## iter 220 value 438.455061
## iter 230 value 430.930929
## iter 240 value 415.480240
## iter 250 value 394.001123
## iter 260 value 387.039195
## iter 270 value 384.757568
## iter 280 value 382.687809
## iter 290 value 379.690969
## iter 300 value 378.106648
## iter 310 value 377.187713
## iter 320 value 375.975369
## iter 330 value 374.850924
## iter 340 value 374.210535
## iter 350 value 373.882347
## iter 360 value 373.618886
## iter 370 value 373.397226
## iter 380 value 373.393873
## iter 390 value 373.385757
## iter 400 value 373.367752
## iter 410 value 373.349413
## iter 420 value 373.338195
## iter 430 value 373.331903
## iter 440 value 373.321640
## iter 450 value 373.295236
## iter 460 value 373.258294
## iter 470 value 372.411457
## iter 480 value 371.348802
## iter 490 value 370.902117
## iter 500 value 370.609300
## final  value 370.609300 
## stopped after 500 iterations
## # weights:  181
## initial  value 1381719.005892 
## iter  10 value 1467.172498
## iter  20 value 786.635499
## iter  30 value 620.697677
## iter  40 value 515.593629
## iter  50 value 431.749592
## iter  60 value 362.889480
## iter  70 value 319.911837
## iter  80 value 275.422756
## iter  90 value 248.714420
## iter 100 value 230.661755
## iter 110 value 212.389696
## iter 120 value 200.509538
## iter 130 value 188.212657
## iter 140 value 178.478457
## iter 150 value 166.196181
## iter 160 value 155.862943
## iter 170 value 144.901767
## iter 180 value 134.984710
## iter 190 value 128.138283
## iter 200 value 123.781473
## iter 210 value 120.680937
## iter 220 value 116.928077
## iter 230 value 114.145371
## iter 240 value 112.195092
## iter 250 value 110.962329
## iter 260 value 108.867038
## iter 270 value 107.311021
## iter 280 value 106.083067
## iter 290 value 105.115520
## iter 300 value 104.299323
## iter 310 value 103.735284
## iter 320 value 103.177508
## iter 330 value 102.788422
## iter 340 value 102.494317
## iter 350 value 102.231510
## iter 360 value 102.058439
## iter 370 value 101.984354
## iter 380 value 101.943327
## iter 390 value 101.862886
## iter 400 value 101.732656
## iter 410 value 101.610271
## iter 420 value 101.527765
## iter 430 value 101.409239
## iter 440 value 101.299359
## iter 450 value 101.010618
## iter 460 value 100.526020
## iter 470 value 100.185451
## iter 480 value 99.605011
## iter 490 value 99.192383
## iter 500 value 98.901575
## final  value 98.901575 
## stopped after 500 iterations
## # weights:  241
## initial  value 1406575.679480 
## iter  10 value 1475.842726
## iter  20 value 810.414268
## iter  30 value 656.808281
## iter  40 value 565.655535
## iter  50 value 446.121098
## iter  60 value 353.930487
## iter  70 value 297.073127
## iter  80 value 264.388584
## iter  90 value 236.573331
## iter 100 value 202.989764
## iter 110 value 183.267716
## iter 120 value 169.714214
## iter 130 value 160.201311
## iter 140 value 151.958332
## iter 150 value 144.941023
## iter 160 value 139.131706
## iter 170 value 133.951261
## iter 180 value 129.112631
## iter 190 value 121.705868
## iter 200 value 114.814578
## iter 210 value 109.074391
## iter 220 value 104.472022
## iter 230 value 101.252522
## iter 240 value 97.580645
## iter 250 value 94.504699
## iter 260 value 92.713906
## iter 270 value 91.167732
## iter 280 value 88.978635
## iter 290 value 87.362266
## iter 300 value 86.082114
## iter 310 value 84.627525
## iter 320 value 83.374603
## iter 330 value 81.261878
## iter 340 value 79.186915
## iter 350 value 78.093975
## iter 360 value 76.713596
## iter 370 value 74.926649
## iter 380 value 72.850564
## iter 390 value 71.190179
## iter 400 value 69.651247
## iter 410 value 68.466478
## iter 420 value 67.145333
## iter 430 value 65.799797
## iter 440 value 64.284767
## iter 450 value 63.308524
## iter 460 value 62.314202
## iter 470 value 61.325149
## iter 480 value 60.164964
## iter 490 value 59.507512
## iter 500 value 59.326190
## final  value 59.326190 
## stopped after 500 iterations
## # weights:  25
## initial  value 1383669.468917 
## iter  10 value 19610.500507
## iter  20 value 14471.155774
## iter  30 value 10709.210303
## iter  40 value 10191.043565
## iter  50 value 6046.932930
## iter  60 value 4952.313284
## iter  70 value 3729.353329
## iter  80 value 2648.268330
## iter  90 value 2088.731714
## iter 100 value 1884.026272
## iter 110 value 1676.235137
## iter 120 value 1401.763556
## iter 130 value 1364.790876
## iter 140 value 1355.966270
## iter 150 value 1352.899702
## iter 160 value 1334.776159
## iter 170 value 1315.688009
## iter 180 value 1311.984388
## iter 190 value 1311.759084
## iter 200 value 1311.742942
## iter 210 value 1311.607034
## final  value 1311.605220 
## converged
## # weights:  61
## initial  value 1386502.060133 
## iter  10 value 9740.599426
## iter  20 value 5854.369773
## iter  30 value 4467.394927
## iter  40 value 3972.388546
## iter  50 value 2908.281894
## iter  60 value 1997.864900
## iter  70 value 1432.078601
## iter  80 value 1093.070390
## iter  90 value 925.424098
## iter 100 value 848.214016
## iter 110 value 825.406624
## iter 120 value 813.197770
## iter 130 value 803.037929
## iter 140 value 797.717340
## iter 150 value 796.269914
## iter 160 value 795.568900
## iter 170 value 795.506408
## iter 180 value 795.359322
## iter 190 value 795.229246
## iter 200 value 795.167448
## final  value 795.163462 
## converged
## # weights:  121
## initial  value 1413503.513575 
## iter  10 value 3403.502379
## iter  20 value 1782.465199
## iter  30 value 1394.351447
## iter  40 value 1237.435546
## iter  50 value 1143.684364
## iter  60 value 1095.687901
## iter  70 value 980.102309
## iter  80 value 804.775659
## iter  90 value 727.451692
## iter 100 value 649.523058
## iter 110 value 605.151559
## iter 120 value 581.368095
## iter 130 value 560.948501
## iter 140 value 538.796003
## iter 150 value 525.258830
## iter 160 value 518.871315
## iter 170 value 512.737274
## iter 180 value 510.199211
## iter 190 value 507.535789
## iter 200 value 498.924478
## iter 210 value 492.578750
## iter 220 value 489.662353
## iter 230 value 484.872188
## iter 240 value 477.891235
## iter 250 value 473.021551
## iter 260 value 470.758371
## iter 270 value 468.759570
## iter 280 value 462.677149
## iter 290 value 458.149118
## iter 300 value 455.809965
## iter 310 value 455.062626
## iter 320 value 453.919139
## iter 330 value 452.168341
## iter 340 value 450.792724
## iter 350 value 448.140931
## iter 360 value 445.935086
## iter 370 value 444.882889
## iter 380 value 442.779359
## iter 390 value 441.208245
## iter 400 value 439.674456
## iter 410 value 437.653320
## iter 420 value 436.556541
## iter 430 value 436.216592
## iter 440 value 436.143452
## iter 450 value 436.129581
## iter 460 value 436.129288
## final  value 436.129259 
## converged
## # weights:  181
## initial  value 1341344.982892 
## iter  10 value 1178.530156
## iter  20 value 857.914268
## iter  30 value 716.172612
## iter  40 value 585.632401
## iter  50 value 517.967504
## iter  60 value 491.344751
## iter  70 value 453.233898
## iter  80 value 434.577200
## iter  90 value 419.083770
## iter 100 value 402.736674
## iter 110 value 391.811097
## iter 120 value 380.858834
## iter 130 value 373.391089
## iter 140 value 366.968491
## iter 150 value 364.547728
## iter 160 value 363.011272
## iter 170 value 362.093043
## iter 180 value 361.032721
## iter 190 value 359.115655
## iter 200 value 356.567070
## iter 210 value 353.191551
## iter 220 value 349.966540
## iter 230 value 347.862241
## iter 240 value 345.343848
## iter 250 value 343.590459
## iter 260 value 342.088736
## iter 270 value 340.313085
## iter 280 value 339.170779
## iter 290 value 337.381680
## iter 300 value 336.310066
## iter 310 value 335.823987
## iter 320 value 335.300823
## iter 330 value 334.938002
## iter 340 value 334.610412
## iter 350 value 334.047338
## iter 360 value 331.861163
## iter 370 value 331.408506
## iter 380 value 330.989204
## iter 390 value 330.317282
## iter 400 value 329.960552
## iter 410 value 329.811912
## iter 420 value 329.626049
## iter 430 value 329.559200
## iter 440 value 329.405436
## iter 450 value 328.916151
## iter 460 value 328.223767
## iter 470 value 327.861521
## iter 480 value 327.777054
## iter 490 value 327.729097
## iter 500 value 327.714027
## final  value 327.714027 
## stopped after 500 iterations
## # weights:  241
## initial  value 1423336.286128 
## iter  10 value 1851.926684
## iter  20 value 1016.069858
## iter  30 value 876.867018
## iter  40 value 713.671169
## iter  50 value 644.062703
## iter  60 value 605.523475
## iter  70 value 572.504108
## iter  80 value 546.979620
## iter  90 value 512.908416
## iter 100 value 490.775363
## iter 110 value 480.697795
## iter 120 value 472.127756
## iter 130 value 465.530078
## iter 140 value 460.845053
## iter 150 value 455.078009
## iter 160 value 450.433695
## iter 170 value 446.406496
## iter 180 value 443.244537
## iter 190 value 439.943566
## iter 200 value 437.041391
## iter 210 value 434.357852
## iter 220 value 431.906909
## iter 230 value 429.154806
## iter 240 value 421.561884
## iter 250 value 416.313930
## iter 260 value 408.862144
## iter 270 value 403.088491
## iter 280 value 395.989093
## iter 290 value 387.612911
## iter 300 value 380.353348
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## iter 470 value 335.804255
## iter 480 value 334.177821
## iter 490 value 333.308001
## iter 500 value 332.497097
## final  value 332.497097 
## stopped after 500 iterations
## # weights:  25
## initial  value 1375188.420772 
## iter  10 value 16119.511031
## iter  20 value 15880.421201
## iter  30 value 15062.229959
## iter  40 value 13267.068718
## iter  50 value 11659.520583
## iter  60 value 11489.160385
## iter  70 value 11184.906299
## iter  80 value 11021.728167
## iter  90 value 9136.261094
## iter 100 value 7171.549235
## iter 110 value 3412.599879
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## iter 130 value 2761.127256
## iter 140 value 1604.437857
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## iter 160 value 1049.019514
## iter 170 value 1029.997241
## iter 180 value 1028.590950
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## iter 200 value 1012.277231
## iter 210 value 1010.768156
## iter 220 value 997.385867
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## iter 240 value 991.860910
## iter 250 value 967.457156
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## iter 300 value 962.087847
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## iter 320 value 911.079431
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## iter 340 value 896.786485
## iter 350 value 896.011580
## iter 360 value 894.410254
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## iter 470 value 824.266305
## iter 480 value 819.949615
## iter 490 value 818.219794
## iter 500 value 817.865796
## final  value 817.865796 
## stopped after 500 iterations
## # weights:  61
## initial  value 1399511.976977 
## iter  10 value 3294.217753
## iter  20 value 1040.022472
## iter  30 value 864.268705
## iter  40 value 792.876915
## iter  50 value 732.249784
## iter  60 value 696.854187
## iter  70 value 657.797294
## iter  80 value 633.222310
## iter  90 value 622.959040
## iter 100 value 606.550763
## iter 110 value 595.308315
## iter 120 value 585.281760
## iter 130 value 582.658232
## iter 140 value 580.469537
## iter 150 value 577.411224
## iter 160 value 573.015636
## iter 170 value 568.183093
## iter 180 value 563.184305
## iter 190 value 557.760849
## iter 200 value 556.321240
## iter 210 value 553.737665
## iter 220 value 551.685934
## iter 230 value 550.088426
## iter 240 value 549.774181
## iter 250 value 549.538638
## iter 260 value 549.521831
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## iter 280 value 549.130537
## iter 290 value 548.268939
## iter 300 value 547.965162
## iter 310 value 547.647210
## iter 320 value 547.448586
## iter 330 value 547.394297
## iter 340 value 547.375884
## iter 350 value 547.372380
## iter 360 value 547.370778
## final  value 547.370379 
## converged
## # weights:  121
## initial  value 1318817.049339 
## iter  10 value 1453.391468
## iter  20 value 921.729845
## iter  30 value 703.977163
## iter  40 value 593.417066
## iter  50 value 550.198590
## iter  60 value 505.727508
## iter  70 value 468.703416
## iter  80 value 443.217625
## iter  90 value 424.657523
## iter 100 value 403.152947
## iter 110 value 383.859944
## iter 120 value 372.970880
## iter 130 value 364.821421
## iter 140 value 354.805915
## iter 150 value 349.642399
## iter 160 value 344.135042
## iter 170 value 340.856247
## iter 180 value 339.335419
## iter 190 value 337.513490
## iter 200 value 335.723795
## iter 210 value 334.423805
## iter 220 value 333.553679
## iter 230 value 331.104395
## iter 240 value 328.718307
## iter 250 value 328.180533
## iter 260 value 327.685124
## iter 270 value 326.494534
## iter 280 value 325.364988
## iter 290 value 324.406136
## iter 300 value 322.301884
## iter 310 value 320.781151
## iter 320 value 318.420135
## iter 330 value 316.643753
## iter 340 value 314.956362
## iter 350 value 313.183301
## iter 360 value 311.139530
## iter 370 value 310.386860
## iter 380 value 309.764422
## iter 390 value 308.243278
## iter 400 value 308.018837
## iter 410 value 307.967262
## iter 420 value 307.952379
## iter 430 value 307.909690
## iter 440 value 307.890632
## iter 450 value 307.884472
## iter 460 value 307.883309
## iter 470 value 307.883103
## iter 480 value 307.882997
## iter 480 value 307.882995
## iter 480 value 307.882995
## final  value 307.882995 
## converged
## # weights:  181
## initial  value 1419080.413303 
## iter  10 value 1100.036839
## iter  20 value 764.218444
## iter  30 value 628.233927
## iter  40 value 496.085788
## iter  50 value 417.574751
## iter  60 value 378.683439
## iter  70 value 317.964373
## iter  80 value 271.928488
## iter  90 value 246.929342
## iter 100 value 231.459760
## iter 110 value 219.348598
## iter 120 value 210.202946
## iter 130 value 202.782278
## iter 140 value 193.502618
## iter 150 value 186.078258
## iter 160 value 180.239895
## iter 170 value 175.942782
## iter 180 value 171.271571
## iter 190 value 168.306903
## iter 200 value 164.965320
## iter 210 value 162.798508
## iter 220 value 160.623448
## iter 230 value 159.490941
## iter 240 value 158.087511
## iter 250 value 155.869789
## iter 260 value 154.031411
## iter 270 value 152.792370
## iter 280 value 151.734751
## iter 290 value 149.337524
## iter 300 value 146.053316
## iter 310 value 140.735076
## iter 320 value 137.470049
## iter 330 value 135.058348
## iter 340 value 133.284646
## iter 350 value 131.965696
## iter 360 value 130.674931
## iter 370 value 130.267534
## iter 380 value 130.089457
## iter 390 value 129.680118
## iter 400 value 129.206069
## iter 410 value 128.657836
## iter 420 value 127.952362
## iter 430 value 127.250826
## iter 440 value 126.691712
## iter 450 value 125.611973
## iter 460 value 124.752862
## iter 470 value 123.890380
## iter 480 value 123.030975
## iter 490 value 122.161475
## iter 500 value 121.987209
## final  value 121.987209 
## stopped after 500 iterations
## # weights:  241
## initial  value 1397038.290086 
## iter  10 value 1211.378573
## iter  20 value 773.976683
## iter  30 value 590.106615
## iter  40 value 447.123158
## iter  50 value 356.949966
## iter  60 value 317.098232
## iter  70 value 282.110668
## iter  80 value 253.652867
## iter  90 value 230.618855
## iter 100 value 210.181163
## iter 110 value 190.277420
## iter 120 value 178.968309
## iter 130 value 168.359989
## iter 140 value 160.503202
## iter 150 value 154.950933
## iter 160 value 150.330774
## iter 170 value 146.556252
## iter 180 value 138.400497
## iter 190 value 130.060627
## iter 200 value 122.363227
## iter 210 value 114.359536
## iter 220 value 109.616771
## iter 230 value 105.506047
## iter 240 value 99.172951
## iter 250 value 95.629248
## iter 260 value 92.842606
## iter 270 value 89.888847
## iter 280 value 87.361913
## iter 290 value 85.397006
## iter 300 value 84.127628
## iter 310 value 82.626720
## iter 320 value 81.320245
## iter 330 value 79.712122
## iter 340 value 78.589361
## iter 350 value 77.536845
## iter 360 value 76.627702
## iter 370 value 76.035352
## iter 380 value 75.369379
## iter 390 value 74.844172
## iter 400 value 74.362881
## iter 410 value 73.927568
## iter 420 value 73.509443
## iter 430 value 73.182551
## iter 440 value 72.812802
## iter 450 value 72.452923
## iter 460 value 72.044837
## iter 470 value 71.591822
## iter 480 value 71.265800
## iter 490 value 71.136214
## iter 500 value 71.103190
## final  value 71.103190 
## stopped after 500 iterations
## # weights:  25
## initial  value 1403525.145478 
## iter  10 value 21688.413349
## iter  20 value 18074.027468
## iter  30 value 13253.667308
## iter  40 value 5355.576130
## iter  50 value 4198.805374
## iter  60 value 2884.583194
## iter  70 value 1664.628362
## iter  80 value 1285.733297
## iter  90 value 1216.410422
## iter 100 value 1197.994005
## iter 110 value 1144.330453
## iter 120 value 1108.928339
## iter 130 value 1074.456607
## iter 140 value 1055.127880
## iter 150 value 1051.987813
## iter 160 value 1004.515674
## iter 170 value 961.179760
## iter 180 value 952.693826
## iter 190 value 943.785455
## iter 200 value 943.700237
## iter 210 value 937.173331
## iter 220 value 930.391885
## iter 230 value 929.316788
## iter 240 value 928.531134
## iter 250 value 928.530112
## iter 260 value 928.524564
## final  value 928.524492 
## converged
## # weights:  61
## initial  value 1366580.530735 
## iter  10 value 4025.099451
## iter  20 value 3381.118706
## iter  30 value 3067.156812
## iter  40 value 2276.477332
## iter  50 value 1789.455966
## iter  60 value 1412.358155
## iter  70 value 1180.183655
## iter  80 value 1128.216822
## iter  90 value 1108.572847
## iter 100 value 1095.400367
## iter 110 value 1086.631911
## iter 120 value 1080.125022
## iter 130 value 1078.936110
## iter 140 value 1078.142800
## iter 150 value 1075.182631
## iter 160 value 1072.198550
## iter 170 value 1070.464567
## iter 180 value 1069.251143
## iter 190 value 1067.745711
## iter 200 value 1064.645319
## iter 210 value 1063.811087
## iter 220 value 1059.156459
## iter 230 value 958.537170
## iter 240 value 835.448039
## iter 250 value 768.284908
## iter 260 value 759.983483
## iter 270 value 757.164801
## iter 280 value 756.291263
## iter 290 value 756.122870
## iter 300 value 754.684904
## iter 310 value 753.736673
## iter 320 value 748.001538
## iter 330 value 747.739831
## iter 340 value 746.181934
## iter 350 value 742.723602
## iter 360 value 738.673894
## iter 370 value 737.319722
## iter 380 value 736.376307
## iter 390 value 734.981637
## iter 400 value 734.068286
## iter 410 value 734.037284
## iter 420 value 733.678072
## iter 430 value 731.830443
## iter 440 value 729.350139
## iter 450 value 729.025183
## iter 460 value 728.941798
## iter 470 value 728.760947
## iter 480 value 728.617584
## iter 490 value 728.443569
## iter 500 value 728.165814
## final  value 728.165814 
## stopped after 500 iterations
## # weights:  121
## initial  value 1428452.421636 
## iter  10 value 6383.787906
## iter  20 value 2051.752399
## iter  30 value 1125.636217
## iter  40 value 874.564366
## iter  50 value 691.287940
## iter  60 value 590.589031
## iter  70 value 531.690486
## iter  80 value 461.855606
## iter  90 value 441.176574
## iter 100 value 402.723929
## iter 110 value 391.707042
## iter 120 value 382.528883
## iter 130 value 373.526151
## iter 140 value 363.883340
## iter 150 value 355.555027
## iter 160 value 344.479368
## iter 170 value 341.230173
## iter 180 value 338.384354
## iter 190 value 333.268233
## iter 200 value 328.019198
## iter 210 value 322.607345
## iter 220 value 317.512169
## iter 230 value 315.307060
## iter 240 value 312.647665
## iter 250 value 311.712684
## iter 260 value 311.293612
## iter 270 value 310.587679
## iter 280 value 309.090897
## iter 290 value 304.860667
## iter 300 value 299.562820
## iter 310 value 293.597498
## iter 320 value 291.253364
## iter 330 value 290.269132
## iter 340 value 289.006626
## iter 350 value 287.202325
## iter 360 value 285.613001
## iter 370 value 284.064400
## iter 380 value 282.898085
## iter 390 value 282.443246
## iter 400 value 282.230639
## iter 410 value 282.132799
## iter 420 value 282.024003
## iter 430 value 281.762204
## iter 440 value 281.347235
## iter 450 value 281.232505
## iter 460 value 281.178753
## iter 470 value 281.117442
## iter 480 value 281.056637
## iter 490 value 281.021164
## iter 500 value 281.019280
## final  value 281.019280 
## stopped after 500 iterations
## # weights:  181
## initial  value 1404235.414039 
## iter  10 value 1033.468879
## iter  20 value 732.040510
## iter  30 value 591.746626
## iter  40 value 449.667585
## iter  50 value 378.525598
## iter  60 value 339.014724
## iter  70 value 295.532887
## iter  80 value 263.315277
## iter  90 value 243.927583
## iter 100 value 225.755741
## iter 110 value 204.605987
## iter 120 value 182.767911
## iter 130 value 171.237414
## iter 140 value 164.273453
## iter 150 value 156.734336
## iter 160 value 152.035480
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## iter 180 value 143.038288
## iter 190 value 138.596944
## iter 200 value 133.930733
## iter 210 value 131.345534
## iter 220 value 127.222502
## iter 230 value 122.129936
## iter 240 value 116.024774
## iter 250 value 113.661619
## iter 260 value 111.375504
## iter 270 value 108.863356
## iter 280 value 107.291148
## iter 290 value 104.740459
## iter 300 value 102.986607
## iter 310 value 101.241333
## iter 320 value 99.959635
## iter 330 value 98.152128
## iter 340 value 97.034264
## iter 350 value 96.205096
## iter 360 value 95.476872
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## iter 380 value 95.108877
## iter 390 value 94.853611
## iter 400 value 94.569762
## iter 410 value 94.297014
## iter 420 value 93.868959
## iter 430 value 93.584574
## iter 440 value 93.292902
## iter 450 value 93.130017
## iter 460 value 92.906305
## iter 470 value 92.652221
## iter 480 value 92.393109
## iter 490 value 92.129343
## iter 500 value 91.597290
## final  value 91.597290 
## stopped after 500 iterations
## # weights:  241
## initial  value 1297007.387561 
## iter  10 value 1363.616777
## iter  20 value 845.703911
## iter  30 value 619.801324
## iter  40 value 507.371582
## iter  50 value 383.606302
## iter  60 value 305.571781
## iter  70 value 261.754083
## iter  80 value 230.270573
## iter  90 value 199.153503
## iter 100 value 183.251919
## iter 110 value 170.155944
## iter 120 value 160.549910
## iter 130 value 151.955758
## iter 140 value 142.364687
## iter 150 value 131.690607
## iter 160 value 126.327951
## iter 170 value 121.897241
## iter 180 value 118.442321
## iter 190 value 113.655079
## iter 200 value 107.127937
## iter 210 value 101.169381
## iter 220 value 94.170370
## iter 230 value 89.445134
## iter 240 value 86.063176
## iter 250 value 83.819712
## iter 260 value 81.852320
## iter 270 value 79.693812
## iter 280 value 78.112753
## iter 290 value 76.116949
## iter 300 value 73.833694
## iter 310 value 71.926612
## iter 320 value 69.963229
## iter 330 value 68.019065
## iter 340 value 66.204908
## iter 350 value 63.593963
## iter 360 value 61.011151
## iter 370 value 58.862093
## iter 380 value 55.888522
## iter 390 value 53.943839
## iter 400 value 52.251507
## iter 410 value 51.348918
## iter 420 value 50.005017
## iter 430 value 48.091941
## iter 440 value 46.922592
## iter 450 value 46.164040
## iter 460 value 45.715342
## iter 470 value 45.412681
## iter 480 value 45.125349
## iter 490 value 44.987267
## iter 500 value 44.956544
## final  value 44.956544 
## stopped after 500 iterations
## # weights:  25
## initial  value 1401352.810833 
## iter  10 value 22584.803653
## iter  20 value 12571.827010
## iter  30 value 10999.636648
## iter  40 value 9299.545654
## iter  50 value 8684.218053
## iter  60 value 8546.295431
## iter  70 value 8479.953727
## final  value 8479.420862 
## converged
## # weights:  61
## initial  value 1390331.906384 
## iter  10 value 32521.360425
## iter  20 value 15876.235844
## iter  30 value 8350.475469
## iter  40 value 4937.028221
## iter  50 value 4263.014694
## iter  60 value 3876.746316
## iter  70 value 3580.612860
## iter  80 value 3502.297249
## iter  90 value 3441.276382
## iter 100 value 3330.367434
## iter 110 value 2999.424938
## iter 120 value 2562.424333
## iter 130 value 2414.360468
## iter 140 value 2246.654949
## iter 150 value 2124.622361
## iter 160 value 1926.190230
## iter 170 value 1834.007432
## iter 180 value 1774.641306
## iter 190 value 1761.455676
## iter 200 value 1757.333466
## iter 210 value 1757.055421
## iter 220 value 1756.354926
## iter 230 value 1751.533501
## iter 240 value 1751.212528
## iter 250 value 1751.068019
## iter 260 value 1750.968093
## iter 270 value 1750.009293
## iter 280 value 1748.692068
## iter 290 value 1747.650787
## iter 300 value 1747.302558
## iter 310 value 1746.756220
## iter 320 value 1745.150726
## iter 330 value 1744.558327
## iter 340 value 1744.449313
## iter 350 value 1744.253696
## iter 360 value 1744.233701
## iter 370 value 1744.205713
## iter 380 value 1744.198107
## iter 390 value 1744.177084
## iter 400 value 1744.001399
## iter 410 value 1743.963266
## iter 420 value 1743.833483
## iter 430 value 1743.515871
## iter 440 value 1742.289992
## iter 450 value 1724.985480
## iter 460 value 1670.950687
## iter 470 value 1648.410435
## iter 480 value 1645.706704
## iter 490 value 1643.629749
## iter 500 value 1639.814294
## final  value 1639.814294 
## stopped after 500 iterations
## # weights:  121
## initial  value 1372741.224659 
## iter  10 value 1361.814825
## iter  20 value 828.234699
## iter  30 value 693.247544
## iter  40 value 603.718984
## iter  50 value 541.420420
## iter  60 value 463.671061
## iter  70 value 416.380510
## iter  80 value 386.960445
## iter  90 value 365.384481
## iter 100 value 352.246981
## iter 110 value 337.449557
## iter 120 value 325.041891
## iter 130 value 306.198126
## iter 140 value 291.685324
## iter 150 value 284.360799
## iter 160 value 274.622291
## iter 170 value 269.009801
## iter 180 value 264.440117
## iter 190 value 261.392059
## iter 200 value 259.624470
## iter 210 value 256.351135
## iter 220 value 252.850472
## iter 230 value 250.261778
## iter 240 value 247.486243
## iter 250 value 246.396741
## iter 260 value 246.035204
## iter 270 value 245.002431
## iter 280 value 243.260762
## iter 290 value 239.471442
## iter 300 value 234.747539
## iter 310 value 230.215330
## iter 320 value 224.970118
## iter 330 value 219.633944
## iter 340 value 214.547526
## iter 350 value 210.370115
## iter 360 value 206.715096
## iter 370 value 204.697407
## iter 380 value 202.399961
## iter 390 value 200.251561
## iter 400 value 198.495495
## iter 410 value 196.873847
## iter 420 value 196.155872
## iter 430 value 195.430962
## iter 440 value 194.930192
## iter 450 value 194.738119
## iter 460 value 194.540000
## iter 470 value 194.452072
## iter 480 value 194.402534
## iter 490 value 194.319312
## iter 500 value 194.315074
## final  value 194.315074 
## stopped after 500 iterations
## # weights:  181
## initial  value 1378444.892063 
## iter  10 value 1918.274623
## iter  20 value 716.689974
## iter  30 value 530.492845
## iter  40 value 414.374758
## iter  50 value 323.227494
## iter  60 value 274.052553
## iter  70 value 245.623020
## iter  80 value 220.803052
## iter  90 value 201.943922
## iter 100 value 188.866415
## iter 110 value 179.686226
## iter 120 value 171.599604
## iter 130 value 164.153187
## iter 140 value 160.946160
## iter 150 value 157.536962
## iter 160 value 154.094919
## iter 170 value 151.303310
## iter 180 value 148.574257
## iter 190 value 146.236953
## iter 200 value 142.951689
## iter 210 value 140.351788
## iter 220 value 138.670972
## iter 230 value 136.804348
## iter 240 value 135.329422
## iter 250 value 133.580671
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## iter 280 value 128.264784
## iter 290 value 127.043162
## iter 300 value 125.636638
## iter 310 value 124.416214
## iter 320 value 123.272124
## iter 330 value 122.006957
## iter 340 value 121.146803
## iter 350 value 120.014727
## iter 360 value 119.041680
## iter 370 value 118.625094
## iter 380 value 118.532871
## iter 390 value 118.393868
## iter 400 value 118.150351
## iter 410 value 117.948265
## iter 420 value 117.474608
## iter 430 value 117.164769
## iter 440 value 116.999823
## iter 450 value 116.798173
## iter 460 value 116.553135
## iter 470 value 116.217539
## iter 480 value 115.908564
## iter 490 value 115.530677
## iter 500 value 114.932059
## final  value 114.932059 
## stopped after 500 iterations
## # weights:  241
## initial  value 1365942.690459 
## iter  10 value 1202.714914
## iter  20 value 756.406976
## iter  30 value 640.140190
## iter  40 value 540.410517
## iter  50 value 440.159302
## iter  60 value 374.215397
## iter  70 value 341.029373
## iter  80 value 285.824140
## iter  90 value 234.919689
## iter 100 value 205.594173
## iter 110 value 184.834819
## iter 120 value 171.595806
## iter 130 value 161.194909
## iter 140 value 153.437564
## iter 150 value 147.235293
## iter 160 value 139.551090
## iter 170 value 129.336066
## iter 180 value 120.819066
## iter 190 value 113.383884
## iter 200 value 107.460461
## iter 210 value 101.088138
## iter 220 value 91.803439
## iter 230 value 83.650749
## iter 240 value 77.978655
## iter 250 value 74.024840
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## iter 270 value 69.464531
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## iter 300 value 60.344949
## iter 310 value 57.002596
## iter 320 value 54.349392
## iter 330 value 52.151034
## iter 340 value 49.925042
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## iter 400 value 44.086062
## iter 410 value 43.879743
## iter 420 value 43.703380
## iter 430 value 43.501966
## iter 440 value 43.331302
## iter 450 value 43.192486
## iter 460 value 42.988365
## iter 470 value 42.853705
## iter 480 value 42.709676
## iter 490 value 42.654831
## iter 500 value 42.641270
## final  value 42.641270 
## stopped after 500 iterations
## # weights:  25
## initial  value 1369311.602739 
## iter  10 value 6820.461951
## iter  20 value 5446.843389
## iter  30 value 5295.517703
## iter  40 value 4906.365573
## iter  50 value 4273.899997
## iter  60 value 3727.361713
## iter  70 value 1766.238133
## iter  80 value 1402.899845
## iter  90 value 1346.814811
## iter 100 value 1328.122741
## iter 110 value 1301.694360
## iter 120 value 1287.661388
## iter 130 value 1282.648408
## iter 140 value 1280.563819
## iter 150 value 1278.920853
## iter 160 value 1278.912377
## final  value 1278.911363 
## converged
## # weights:  61
## initial  value 1377040.387857 
## iter  10 value 8571.316210
## iter  20 value 6387.743023
## iter  30 value 5753.483085
## iter  40 value 5440.730456
## iter  50 value 4714.881211
## iter  60 value 3345.592322
## iter  70 value 2573.088092
## iter  80 value 2317.392581
## iter  90 value 2283.694066
## iter 100 value 2258.226956
## iter 110 value 2225.707491
## iter 120 value 2201.629906
## iter 130 value 2176.421395
## iter 140 value 2137.332954
## iter 150 value 2121.353478
## iter 160 value 2100.527564
## iter 170 value 2000.596293
## iter 180 value 1818.544666
## iter 190 value 1789.426629
## iter 200 value 1787.530658
## iter 210 value 1773.212944
## iter 220 value 1760.690107
## iter 230 value 1749.447440
## iter 240 value 1746.319286
## iter 250 value 1745.545835
## iter 260 value 1744.931662
## iter 270 value 1744.452723
## iter 280 value 1743.533941
## iter 290 value 1717.677648
## iter 300 value 1698.646441
## iter 310 value 1652.563573
## iter 320 value 1644.484688
## iter 330 value 1642.039126
## iter 340 value 1622.964985
## iter 350 value 1603.421679
## iter 360 value 1583.012274
## iter 370 value 1563.759331
## iter 380 value 1512.874144
## iter 390 value 1348.064613
## iter 400 value 1019.306111
## iter 410 value 952.625567
## iter 420 value 912.827907
## iter 430 value 898.958324
## iter 440 value 892.346428
## iter 450 value 868.011342
## iter 460 value 865.222298
## iter 470 value 854.609702
## iter 480 value 848.468739
## iter 490 value 848.260851
## iter 500 value 844.915794
## final  value 844.915794 
## stopped after 500 iterations
## # weights:  121
## initial  value 1426097.473036 
## iter  10 value 3019.134982
## iter  20 value 1534.369030
## iter  30 value 1048.182910
## iter  40 value 762.735738
## iter  50 value 650.644900
## iter  60 value 591.275378
## iter  70 value 558.046179
## iter  80 value 531.574389
## iter  90 value 516.034614
## iter 100 value 491.111659
## iter 110 value 471.965378
## iter 120 value 463.287524
## iter 130 value 454.202281
## iter 140 value 448.749844
## iter 150 value 440.021249
## iter 160 value 421.566852
## iter 170 value 387.683424
## iter 180 value 363.415541
## iter 190 value 352.033622
## iter 200 value 339.449921
## iter 210 value 330.497241
## iter 220 value 320.776998
## iter 230 value 311.420873
## iter 240 value 307.067459
## iter 250 value 304.823497
## iter 260 value 303.881504
## iter 270 value 302.709914
## iter 280 value 301.279212
## iter 290 value 300.125644
## iter 300 value 298.034447
## iter 310 value 295.206864
## iter 320 value 291.522073
## iter 330 value 285.937588
## iter 340 value 280.633593
## iter 350 value 274.581599
## iter 360 value 269.913173
## iter 370 value 268.154113
## iter 380 value 267.271312
## iter 390 value 266.459392
## iter 400 value 265.622675
## iter 410 value 265.390142
## iter 420 value 264.988448
## iter 430 value 264.924035
## iter 440 value 264.911706
## iter 450 value 264.892737
## iter 460 value 264.807567
## iter 470 value 264.470707
## iter 480 value 264.101405
## iter 490 value 263.905791
## iter 500 value 263.901083
## final  value 263.901083 
## stopped after 500 iterations
## # weights:  181
## initial  value 1386961.737673 
## iter  10 value 1078.049854
## iter  20 value 742.927129
## iter  30 value 636.335191
## iter  40 value 520.049415
## iter  50 value 415.406605
## iter  60 value 363.600726
## iter  70 value 322.840754
## iter  80 value 288.649996
## iter  90 value 259.843976
## iter 100 value 241.239006
## iter 110 value 226.494650
## iter 120 value 216.959256
## iter 130 value 204.070140
## iter 140 value 194.045034
## iter 150 value 186.977582
## iter 160 value 180.402468
## iter 170 value 174.068486
## iter 180 value 167.872786
## iter 190 value 163.612426
## iter 200 value 160.190966
## iter 210 value 155.173493
## iter 220 value 149.317242
## iter 230 value 143.899695
## iter 240 value 139.524960
## iter 250 value 136.284133
## iter 260 value 133.210503
## iter 270 value 131.087484
## iter 280 value 129.408533
## iter 290 value 127.455007
## iter 300 value 125.898964
## iter 310 value 124.852046
## iter 320 value 123.913197
## iter 330 value 122.251699
## iter 340 value 121.052997
## iter 350 value 120.318208
## iter 360 value 119.802849
## iter 370 value 119.478780
## iter 380 value 119.317299
## iter 390 value 119.151819
## iter 400 value 118.567606
## iter 410 value 117.932442
## iter 420 value 117.282406
## iter 430 value 116.378003
## iter 440 value 115.116117
## iter 450 value 113.857906
## iter 460 value 112.988796
## iter 470 value 112.437231
## iter 480 value 111.998950
## iter 490 value 111.572933
## iter 500 value 110.859297
## final  value 110.859297 
## stopped after 500 iterations
## # weights:  241
## initial  value 1330168.016794 
## iter  10 value 1354.057588
## iter  20 value 735.288960
## iter  30 value 597.233265
## iter  40 value 461.802342
## iter  50 value 359.972501
## iter  60 value 289.409395
## iter  70 value 242.612727
## iter  80 value 209.506249
## iter  90 value 186.474628
## iter 100 value 169.072803
## iter 110 value 153.979989
## iter 120 value 141.134093
## iter 130 value 127.000045
## iter 140 value 116.141903
## iter 150 value 109.717238
## iter 160 value 104.958974
## iter 170 value 97.674274
## iter 180 value 91.368282
## iter 190 value 85.742121
## iter 200 value 81.929063
## iter 210 value 78.274056
## iter 220 value 74.237042
## iter 230 value 67.501049
## iter 240 value 62.110533
## iter 250 value 58.471807
## iter 260 value 54.540970
## iter 270 value 50.742982
## iter 280 value 47.718846
## iter 290 value 45.321280
## iter 300 value 43.246547
## iter 310 value 41.025847
## iter 320 value 38.058901
## iter 330 value 35.478419
## iter 340 value 33.212874
## iter 350 value 30.464264
## iter 360 value 28.765378
## iter 370 value 27.351663
## iter 380 value 25.782100
## iter 390 value 24.665799
## iter 400 value 23.769805
## iter 410 value 23.257431
## iter 420 value 22.891011
## iter 430 value 22.369908
## iter 440 value 21.935227
## iter 450 value 21.638614
## iter 460 value 21.338875
## iter 470 value 20.627857
## iter 480 value 20.050029
## iter 490 value 19.814863
## iter 500 value 19.753910
## final  value 19.753910 
## stopped after 500 iterations
## # weights:  25
## initial  value 1356805.638731 
## iter  10 value 75264.400144
## iter  20 value 27260.137525
## iter  30 value 10308.660240
## iter  40 value 7848.320484
## iter  50 value 6828.745253
## iter  60 value 5670.820930
## iter  70 value 4079.676668
## iter  80 value 3184.411883
## iter  90 value 2451.642550
## iter 100 value 1872.174980
## iter 110 value 1696.033481
## iter 120 value 1660.900048
## iter 130 value 1632.964370
## iter 140 value 1573.871316
## iter 150 value 1482.148184
## iter 160 value 1478.769317
## iter 170 value 1478.278132
## iter 180 value 1477.975241
## iter 190 value 1476.658226
## final  value 1476.608604 
## converged
## # weights:  61
## initial  value 1366095.037300 
## iter  10 value 15500.230585
## iter  20 value 6932.150124
## iter  30 value 5246.628694
## iter  40 value 4242.189076
## iter  50 value 3329.164679
## iter  60 value 2366.227537
## iter  70 value 1708.938104
## iter  80 value 1391.636327
## iter  90 value 1232.280619
## iter 100 value 1118.446332
## iter 110 value 1055.573254
## iter 120 value 1039.652446
## iter 130 value 1029.778817
## iter 140 value 1013.329635
## iter 150 value 965.409263
## iter 160 value 930.003816
## iter 170 value 898.710952
## iter 180 value 865.355531
## iter 190 value 819.846356
## iter 200 value 782.378552
## iter 210 value 763.745785
## iter 220 value 745.942866
## iter 230 value 735.137048
## iter 240 value 726.138018
## iter 250 value 722.721128
## iter 260 value 721.120291
## iter 270 value 718.594266
## iter 280 value 715.903850
## iter 290 value 714.092672
## iter 300 value 713.335423
## iter 310 value 713.095898
## final  value 713.071163 
## converged
## # weights:  121
## initial  value 1409075.898309 
## iter  10 value 1847.817905
## iter  20 value 1068.200220
## iter  30 value 893.294344
## iter  40 value 826.486051
## iter  50 value 762.860846
## iter  60 value 717.960527
## iter  70 value 680.295368
## iter  80 value 639.636964
## iter  90 value 610.556957
## iter 100 value 588.675552
## iter 110 value 574.373950
## iter 120 value 558.432178
## iter 130 value 538.771954
## iter 140 value 525.489361
## iter 150 value 517.690046
## iter 160 value 512.989313
## iter 170 value 506.994262
## iter 180 value 502.948438
## iter 190 value 499.307031
## iter 200 value 497.387117
## iter 210 value 495.900498
## iter 220 value 494.245553
## iter 230 value 492.500804
## iter 240 value 491.115169
## iter 250 value 490.250983
## iter 260 value 489.864965
## iter 270 value 489.276606
## iter 280 value 489.005309
## iter 290 value 488.765986
## iter 300 value 488.709992
## iter 310 value 488.689976
## iter 320 value 488.679460
## iter 330 value 488.673236
## iter 340 value 488.518832
## iter 350 value 488.160426
## iter 360 value 488.092817
## iter 370 value 488.081131
## final  value 488.081013 
## converged
## # weights:  181
## initial  value 1420846.970810 
## iter  10 value 1677.569524
## iter  20 value 926.712207
## iter  30 value 722.281281
## iter  40 value 645.123054
## iter  50 value 563.440128
## iter  60 value 529.305134
## iter  70 value 516.971043
## iter  80 value 507.290514
## iter  90 value 496.210461
## iter 100 value 486.665287
## iter 110 value 479.418876
## iter 120 value 474.656296
## iter 130 value 468.756272
## iter 140 value 458.049033
## iter 150 value 448.012822
## iter 160 value 437.367768
## iter 170 value 427.826989
## iter 180 value 421.223473
## iter 190 value 418.060853
## iter 200 value 415.888567
## iter 210 value 411.772442
## iter 220 value 407.892739
## iter 230 value 405.791094
## iter 240 value 403.604615
## iter 250 value 402.023665
## iter 260 value 400.749612
## iter 270 value 399.811389
## iter 280 value 399.011901
## iter 290 value 398.163161
## iter 300 value 397.129480
## iter 310 value 396.220651
## iter 320 value 395.269558
## iter 330 value 394.567892
## iter 340 value 393.845009
## iter 350 value 393.280812
## iter 360 value 392.912667
## iter 370 value 392.607859
## iter 380 value 392.233342
## iter 390 value 391.642458
## iter 400 value 391.226363
## iter 410 value 390.869277
## iter 420 value 390.507116
## iter 430 value 390.241278
## iter 440 value 389.886887
## iter 450 value 388.460695
## iter 460 value 386.689697
## iter 470 value 385.321888
## iter 480 value 383.310046
## iter 490 value 380.175348
## iter 500 value 377.556239
## final  value 377.556239 
## stopped after 500 iterations
## # weights:  241
## initial  value 1440672.319555 
## iter  10 value 1623.988543
## iter  20 value 940.362169
## iter  30 value 746.413832
## iter  40 value 640.447421
## iter  50 value 572.005881
## iter  60 value 521.119817
## iter  70 value 488.603134
## iter  80 value 464.232070
## iter  90 value 434.453714
## iter 100 value 417.009765
## iter 110 value 405.767907
## iter 120 value 399.750038
## iter 130 value 393.649568
## iter 140 value 389.200958
## iter 150 value 385.707892
## iter 160 value 382.554259
## iter 170 value 379.918877
## iter 180 value 377.691438
## iter 190 value 374.921612
## iter 200 value 371.379408
## iter 210 value 366.090511
## iter 220 value 363.051108
## iter 230 value 360.440429
## iter 240 value 357.332464
## iter 250 value 354.616328
## iter 260 value 352.067334
## iter 270 value 349.349318
## iter 280 value 345.615171
## iter 290 value 342.425243
## iter 300 value 339.867529
## iter 310 value 338.380519
## iter 320 value 337.175378
## iter 330 value 335.947895
## iter 340 value 334.856446
## iter 350 value 333.704725
## iter 360 value 332.549502
## iter 370 value 332.048847
## iter 380 value 331.756735
## iter 390 value 331.416512
## iter 400 value 330.325096
## iter 410 value 329.015926
## iter 420 value 327.481508
## iter 430 value 326.520416
## iter 440 value 325.972047
## iter 450 value 325.346204
## iter 460 value 324.790732
## iter 470 value 324.278601
## iter 480 value 323.578269
## iter 490 value 323.258868
## iter 500 value 322.740780
## final  value 322.740780 
## stopped after 500 iterations
## # weights:  25
## initial  value 1402172.750021 
## iter  10 value 63325.462518
## iter  20 value 17374.865040
## iter  30 value 7851.262685
## iter  40 value 7626.392126
## iter  50 value 6850.267576
## iter  60 value 5566.152060
## iter  70 value 4725.894181
## iter  80 value 3978.485308
## iter  90 value 3969.098548
## iter 100 value 3964.586629
## iter 110 value 3897.822752
## iter 120 value 3875.307228
## iter 130 value 3862.255970
## iter 140 value 3854.882489
## iter 150 value 3510.283214
## iter 160 value 2983.364198
## iter 170 value 2155.151247
## iter 180 value 1733.168315
## iter 190 value 1429.036831
## iter 200 value 1311.264154
## iter 210 value 1303.758465
## iter 220 value 1283.137187
## iter 230 value 1282.673322
## iter 240 value 1213.657112
## iter 250 value 1170.855668
## iter 260 value 1151.766948
## iter 270 value 1147.997427
## iter 280 value 1147.215499
## final  value 1147.190345 
## converged
## # weights:  61
## initial  value 1400054.198890 
## iter  10 value 4849.047418
## iter  20 value 1552.762399
## iter  30 value 1225.205040
## iter  40 value 949.977230
## iter  50 value 840.550643
## iter  60 value 793.498010
## iter  70 value 751.506218
## iter  80 value 720.738142
## iter  90 value 696.394507
## iter 100 value 669.001708
## iter 110 value 647.103746
## iter 120 value 628.620741
## iter 130 value 624.584764
## iter 140 value 622.206719
## iter 150 value 617.929340
## iter 160 value 611.620803
## iter 170 value 609.056782
## iter 180 value 595.438423
## iter 190 value 586.192734
## iter 200 value 583.608631
## iter 210 value 581.872718
## iter 220 value 581.102191
## iter 230 value 580.674325
## iter 240 value 580.650733
## iter 250 value 580.603061
## iter 260 value 580.512218
## iter 270 value 579.180102
## iter 280 value 578.353066
## iter 290 value 570.205273
## iter 300 value 565.125652
## iter 310 value 563.558467
## iter 320 value 562.761437
## iter 330 value 562.406119
## iter 340 value 562.302868
## iter 350 value 562.286817
## iter 360 value 562.284577
## iter 370 value 562.277496
## iter 380 value 562.230388
## iter 390 value 562.188212
## iter 400 value 562.174503
## iter 410 value 562.171149
## final  value 562.170587 
## converged
## # weights:  121
## initial  value 1358554.334746 
## iter  10 value 1338.043646
## iter  20 value 851.422232
## iter  30 value 709.174966
## iter  40 value 620.502330
## iter  50 value 578.488141
## iter  60 value 533.582756
## iter  70 value 498.647211
## iter  80 value 465.874182
## iter  90 value 436.687549
## iter 100 value 408.599125
## iter 110 value 381.666885
## iter 120 value 360.529755
## iter 130 value 345.929137
## iter 140 value 336.035854
## iter 150 value 323.494814
## iter 160 value 316.865913
## iter 170 value 310.223047
## iter 180 value 304.961407
## iter 190 value 300.516850
## iter 200 value 297.458252
## iter 210 value 295.205302
## iter 220 value 292.894244
## iter 230 value 291.225571
## iter 240 value 290.257770
## iter 250 value 289.800194
## iter 260 value 289.590261
## iter 270 value 289.000019
## iter 280 value 287.961400
## iter 290 value 285.572942
## iter 300 value 279.040888
## iter 310 value 271.858443
## iter 320 value 260.272372
## iter 330 value 250.024629
## iter 340 value 243.181167
## iter 350 value 238.249752
## iter 360 value 235.927820
## iter 370 value 234.576488
## iter 380 value 234.043025
## iter 390 value 232.784801
## iter 400 value 232.382912
## iter 410 value 232.200976
## iter 420 value 232.128865
## iter 430 value 232.041128
## iter 440 value 232.016297
## iter 450 value 232.008511
## iter 460 value 232.006337
## iter 470 value 232.005158
## iter 480 value 232.004205
## final  value 232.003724 
## converged
## # weights:  181
## initial  value 1389840.698867 
## iter  10 value 1193.943373
## iter  20 value 774.036560
## iter  30 value 569.949465
## iter  40 value 464.371061
## iter  50 value 381.404003
## iter  60 value 341.305451
## iter  70 value 309.188409
## iter  80 value 270.540829
## iter  90 value 253.057644
## iter 100 value 239.469340
## iter 110 value 228.362532
## iter 120 value 220.170792
## iter 130 value 209.910687
## iter 140 value 195.954325
## iter 150 value 185.858311
## iter 160 value 176.221664
## iter 170 value 169.398743
## iter 180 value 161.459833
## iter 190 value 152.523597
## iter 200 value 145.079997
## iter 210 value 140.144102
## iter 220 value 138.377094
## iter 230 value 136.092874
## iter 240 value 133.733048
## iter 250 value 132.113689
## iter 260 value 131.084706
## iter 270 value 129.388314
## iter 280 value 127.586687
## iter 290 value 125.492254
## iter 300 value 123.634320
## iter 310 value 121.746325
## iter 320 value 119.770700
## iter 330 value 116.800554
## iter 340 value 114.280010
## iter 350 value 112.137141
## iter 360 value 110.864362
## iter 370 value 110.563737
## iter 380 value 110.452383
## iter 390 value 110.264549
## iter 400 value 109.943746
## iter 410 value 109.704432
## iter 420 value 109.357755
## iter 430 value 108.886792
## iter 440 value 108.303051
## iter 450 value 107.464079
## iter 460 value 106.686670
## iter 470 value 105.821288
## iter 480 value 104.881676
## iter 490 value 104.054904
## iter 500 value 102.918903
## final  value 102.918903 
## stopped after 500 iterations
## # weights:  241
## initial  value 1372968.306651 
## iter  10 value 1170.623995
## iter  20 value 735.632998
## iter  30 value 632.049007
## iter  40 value 544.407516
## iter  50 value 427.962919
## iter  60 value 342.964965
## iter  70 value 287.459051
## iter  80 value 260.112230
## iter  90 value 234.280996
## iter 100 value 206.523434
## iter 110 value 192.114085
## iter 120 value 179.062349
## iter 130 value 166.527489
## iter 140 value 153.064342
## iter 150 value 142.514745
## iter 160 value 134.331668
## iter 170 value 130.198101
## iter 180 value 125.191962
## iter 190 value 119.377881
## iter 200 value 114.618292
## iter 210 value 109.915254
## iter 220 value 103.605734
## iter 230 value 98.510239
## iter 240 value 94.561267
## iter 250 value 90.567594
## iter 260 value 87.981678
## iter 270 value 86.240400
## iter 280 value 84.617589
## iter 290 value 82.273894
## iter 300 value 80.424550
## iter 310 value 78.110941
## iter 320 value 76.476324
## iter 330 value 74.292248
## iter 340 value 72.280307
## iter 350 value 70.222535
## iter 360 value 68.658065
## iter 370 value 67.480433
## iter 380 value 66.035567
## iter 390 value 64.494497
## iter 400 value 63.446779
## iter 410 value 62.564942
## iter 420 value 62.008232
## iter 430 value 61.570939
## iter 440 value 61.002621
## iter 450 value 59.970512
## iter 460 value 59.154846
## iter 470 value 58.221127
## iter 480 value 57.589656
## iter 490 value 57.435851
## iter 500 value 57.343203
## final  value 57.343203 
## stopped after 500 iterations
## # weights:  25
## initial  value 1430460.437131 
## iter  10 value 6192.721907
## iter  20 value 5263.162093
## iter  30 value 5176.136277
## iter  40 value 5168.031669
## iter  50 value 5089.330370
## iter  60 value 4881.674891
## iter  70 value 4454.241373
## iter  80 value 2551.895501
## iter  90 value 1623.266511
## iter 100 value 1403.561932
## iter 110 value 1342.058141
## iter 120 value 1316.912993
## iter 130 value 1294.208753
## iter 140 value 1286.474221
## iter 150 value 1283.094032
## iter 160 value 1281.159469
## iter 170 value 1281.118938
## final  value 1281.115241 
## converged
## # weights:  61
## initial  value 1396403.612522 
## iter  10 value 156801.679997
## iter  20 value 9255.601861
## iter  30 value 5820.746535
## iter  40 value 4175.369343
## iter  50 value 2776.615704
## iter  60 value 2341.844432
## iter  70 value 2258.341534
## iter  80 value 2090.884091
## iter  90 value 1610.385517
## iter 100 value 1365.834262
## iter 110 value 1206.686385
## iter 120 value 1007.860471
## iter 130 value 860.712021
## iter 140 value 817.375927
## iter 150 value 780.292554
## iter 160 value 753.563689
## iter 170 value 736.667218
## iter 180 value 721.136633
## iter 190 value 705.856157
## iter 200 value 687.779513
## iter 210 value 676.467965
## iter 220 value 673.194834
## iter 230 value 660.822517
## iter 240 value 647.708209
## iter 250 value 637.743736
## iter 260 value 629.059467
## iter 270 value 625.919490
## iter 280 value 624.196927
## iter 290 value 622.561522
## iter 300 value 620.361731
## iter 310 value 618.560679
## iter 320 value 617.385723
## iter 330 value 616.722429
## iter 340 value 616.714215
## iter 350 value 616.652881
## iter 360 value 616.578315
## iter 370 value 615.665720
## iter 380 value 614.923055
## iter 390 value 614.485093
## iter 400 value 614.195769
## iter 410 value 614.126088
## iter 420 value 614.098984
## iter 430 value 614.085006
## iter 440 value 614.077636
## iter 450 value 614.074287
## final  value 614.074150 
## converged
## # weights:  121
## initial  value 1388293.363406 
## iter  10 value 1383.165057
## iter  20 value 885.247952
## iter  30 value 695.660265
## iter  40 value 592.360729
## iter  50 value 530.887238
## iter  60 value 479.486363
## iter  70 value 462.548999
## iter  80 value 451.181957
## iter  90 value 435.353896
## iter 100 value 410.620112
## iter 110 value 394.350099
## iter 120 value 385.153318
## iter 130 value 381.087108
## iter 140 value 378.335145
## iter 150 value 372.178297
## iter 160 value 364.028707
## iter 170 value 356.849707
## iter 180 value 348.375675
## iter 190 value 341.628982
## iter 200 value 338.214983
## iter 210 value 330.911257
## iter 220 value 317.532809
## iter 230 value 311.188692
## iter 240 value 301.231592
## iter 250 value 296.364708
## iter 260 value 294.794725
## iter 270 value 290.425789
## iter 280 value 285.353601
## iter 290 value 278.480588
## iter 300 value 272.256375
## iter 310 value 263.844181
## iter 320 value 259.584139
## iter 330 value 255.130565
## iter 340 value 249.075407
## iter 350 value 246.161343
## iter 360 value 242.799917
## iter 370 value 241.242062
## iter 380 value 240.429648
## iter 390 value 240.284215
## iter 400 value 240.150351
## iter 410 value 239.883590
## iter 420 value 239.669712
## iter 430 value 239.613921
## iter 440 value 239.522029
## iter 450 value 239.283229
## iter 460 value 238.991782
## iter 470 value 238.453257
## iter 480 value 237.994992
## iter 490 value 237.738315
## iter 500 value 237.651468
## final  value 237.651468 
## stopped after 500 iterations
## # weights:  181
## initial  value 1359360.663325 
## iter  10 value 1401.921210
## iter  20 value 875.177878
## iter  30 value 656.996690
## iter  40 value 518.820492
## iter  50 value 462.048776
## iter  60 value 405.161586
## iter  70 value 339.862593
## iter  80 value 301.241940
## iter  90 value 274.229864
## iter 100 value 254.383086
## iter 110 value 240.361910
## iter 120 value 226.174053
## iter 130 value 218.524045
## iter 140 value 211.111749
## iter 150 value 201.036837
## iter 160 value 189.585912
## iter 170 value 180.469228
## iter 180 value 170.126855
## iter 190 value 157.838030
## iter 200 value 149.298143
## iter 210 value 143.991844
## iter 220 value 140.057437
## iter 230 value 137.532127
## iter 240 value 136.269383
## iter 250 value 135.180173
## iter 260 value 134.072039
## iter 270 value 132.887361
## iter 280 value 130.873392
## iter 290 value 129.559743
## iter 300 value 128.438907
## iter 310 value 127.581425
## iter 320 value 127.098398
## iter 330 value 126.701337
## iter 340 value 126.482498
## iter 350 value 126.177734
## iter 360 value 125.963367
## iter 370 value 125.891295
## iter 380 value 125.857253
## iter 390 value 125.788713
## iter 400 value 125.710703
## iter 410 value 125.571493
## iter 420 value 125.439709
## iter 430 value 125.125151
## iter 440 value 124.694451
## iter 450 value 124.137828
## iter 460 value 123.722588
## iter 470 value 123.426111
## iter 480 value 123.152354
## iter 490 value 122.335544
## iter 500 value 121.385912
## final  value 121.385912 
## stopped after 500 iterations
## # weights:  241
## initial  value 1339079.578028 
## iter  10 value 2295.304643
## iter  20 value 942.311683
## iter  30 value 713.771595
## iter  40 value 566.067968
## iter  50 value 490.033781
## iter  60 value 430.538978
## iter  70 value 362.603381
## iter  80 value 319.972221
## iter  90 value 293.257829
## iter 100 value 270.366671
## iter 110 value 250.868414
## iter 120 value 232.745818
## iter 130 value 219.992035
## iter 140 value 211.611574
## iter 150 value 201.267775
## iter 160 value 192.145775
## iter 170 value 177.589026
## iter 180 value 166.659839
## iter 190 value 156.404584
## iter 200 value 150.844173
## iter 210 value 145.291709
## iter 220 value 139.904840
## iter 230 value 133.461468
## iter 240 value 128.691452
## iter 250 value 124.241918
## iter 260 value 120.144342
## iter 270 value 115.950384
## iter 280 value 112.089584
## iter 290 value 107.784777
## iter 300 value 103.943651
## iter 310 value 98.856854
## iter 320 value 94.423971
## iter 330 value 89.956071
## iter 340 value 85.707613
## iter 350 value 82.353643
## iter 360 value 79.497769
## iter 370 value 76.804721
## iter 380 value 74.571519
## iter 390 value 73.214677
## iter 400 value 71.836160
## iter 410 value 70.747803
## iter 420 value 69.745380
## iter 430 value 69.151290
## iter 440 value 68.726440
## iter 450 value 68.170816
## iter 460 value 67.481096
## iter 470 value 66.590129
## iter 480 value 65.743445
## iter 490 value 65.329004
## iter 500 value 65.273394
## final  value 65.273394 
## stopped after 500 iterations
## # weights:  25
## initial  value 1371306.977207 
## iter  10 value 6163.431950
## iter  20 value 5740.578549
## iter  30 value 5724.250739
## iter  40 value 5703.430395
## iter  50 value 5673.624462
## final  value 5673.568283 
## converged
## # weights:  61
## initial  value 1356619.584693 
## iter  10 value 2315.114235
## iter  20 value 1665.102298
## iter  30 value 1462.016843
## iter  40 value 1206.160009
## iter  50 value 1086.399977
## iter  60 value 982.460133
## iter  70 value 866.940522
## iter  80 value 773.655021
## iter  90 value 743.286615
## iter 100 value 725.367140
## iter 110 value 716.357390
## iter 120 value 701.828081
## iter 130 value 689.110762
## iter 140 value 684.861526
## iter 150 value 682.639225
## iter 160 value 679.578819
## iter 170 value 677.936305
## iter 180 value 675.877074
## iter 190 value 675.245495
## iter 200 value 675.151190
## iter 210 value 675.003065
## iter 220 value 674.894572
## iter 230 value 674.888210
## iter 240 value 674.878981
## iter 250 value 674.849118
## iter 260 value 674.813656
## iter 270 value 674.759781
## iter 280 value 674.552664
## iter 290 value 674.354239
## iter 300 value 674.115244
## iter 310 value 673.920097
## iter 320 value 673.882352
## iter 330 value 673.869954
## final  value 673.829711 
## converged
## # weights:  121
## initial  value 1414504.383448 
## iter  10 value 1277.136590
## iter  20 value 856.073759
## iter  30 value 696.579075
## iter  40 value 603.528958
## iter  50 value 527.646195
## iter  60 value 450.848424
## iter  70 value 418.231172
## iter  80 value 399.746059
## iter  90 value 388.031543
## iter 100 value 378.898658
## iter 110 value 372.751192
## iter 120 value 363.951122
## iter 130 value 349.706944
## iter 140 value 337.203507
## iter 150 value 327.520597
## iter 160 value 319.423250
## iter 170 value 314.575674
## iter 180 value 311.274687
## iter 190 value 307.789178
## iter 200 value 305.134440
## iter 210 value 303.450601
## iter 220 value 301.233392
## iter 230 value 298.619777
## iter 240 value 296.963919
## iter 250 value 296.434565
## iter 260 value 296.220354
## iter 270 value 295.976852
## iter 280 value 295.180732
## iter 290 value 294.270332
## iter 300 value 293.856354
## iter 310 value 293.733665
## iter 320 value 293.170375
## iter 330 value 290.299040
## iter 340 value 288.018065
## iter 350 value 285.239615
## iter 360 value 280.105272
## iter 370 value 277.792216
## iter 380 value 271.443322
## iter 390 value 267.847396
## iter 400 value 263.243480
## iter 410 value 262.496018
## iter 420 value 261.605023
## iter 430 value 261.354269
## iter 440 value 261.286821
## iter 450 value 261.226228
## iter 460 value 261.127786
## iter 470 value 261.041762
## iter 480 value 261.004344
## iter 490 value 260.896863
## iter 500 value 260.877307
## final  value 260.877307 
## stopped after 500 iterations
## # weights:  181
## initial  value 1386840.610073 
## iter  10 value 1110.872717
## iter  20 value 772.085557
## iter  30 value 628.226471
## iter  40 value 460.964891
## iter  50 value 383.573460
## iter  60 value 341.633726
## iter  70 value 309.692626
## iter  80 value 263.910697
## iter  90 value 233.178115
## iter 100 value 219.739706
## iter 110 value 208.650735
## iter 120 value 196.330145
## iter 130 value 186.609681
## iter 140 value 174.323692
## iter 150 value 159.330326
## iter 160 value 148.764315
## iter 170 value 141.767555
## iter 180 value 134.696091
## iter 190 value 128.460008
## iter 200 value 122.750652
## iter 210 value 118.479390
## iter 220 value 115.432883
## iter 230 value 113.302326
## iter 240 value 111.177109
## iter 250 value 109.010053
## iter 260 value 107.737632
## iter 270 value 106.878072
## iter 280 value 105.427370
## iter 290 value 104.184649
## iter 300 value 103.097125
## iter 310 value 102.170722
## iter 320 value 101.641580
## iter 330 value 101.313430
## iter 340 value 100.576313
## iter 350 value 99.653474
## iter 360 value 99.002302
## iter 370 value 98.713766
## iter 380 value 98.596451
## iter 390 value 98.435520
## iter 400 value 98.137061
## iter 410 value 97.927819
## iter 420 value 97.356260
## iter 430 value 96.681623
## iter 440 value 96.220542
## iter 450 value 95.894381
## iter 460 value 95.586668
## iter 470 value 95.245946
## iter 480 value 94.586872
## iter 490 value 94.017195
## iter 500 value 93.008983
## final  value 93.008983 
## stopped after 500 iterations
## # weights:  241
## initial  value 1399979.248215 
## iter  10 value 3258.873150
## iter  20 value 947.290203
## iter  30 value 665.382210
## iter  40 value 473.081693
## iter  50 value 374.643924
## iter  60 value 318.499178
## iter  70 value 276.049849
## iter  80 value 236.073737
## iter  90 value 207.059490
## iter 100 value 173.152816
## iter 110 value 136.497582
## iter 120 value 118.893924
## iter 130 value 106.574788
## iter 140 value 96.096820
## iter 150 value 89.034156
## iter 160 value 81.835465
## iter 170 value 76.953343
## iter 180 value 70.811236
## iter 190 value 65.132480
## iter 200 value 60.455958
## iter 210 value 56.354792
## iter 220 value 52.486682
## iter 230 value 49.864610
## iter 240 value 47.988067
## iter 250 value 46.385572
## iter 260 value 43.862486
## iter 270 value 42.161818
## iter 280 value 40.899606
## iter 290 value 39.534512
## iter 300 value 38.297851
## iter 310 value 37.051015
## iter 320 value 35.446105
## iter 330 value 34.168357
## iter 340 value 33.033579
## iter 350 value 31.951122
## iter 360 value 30.829325
## iter 370 value 29.631059
## iter 380 value 28.717831
## iter 390 value 27.589415
## iter 400 value 26.797909
## iter 410 value 26.194780
## iter 420 value 25.574603
## iter 430 value 24.900634
## iter 440 value 24.466159
## iter 450 value 24.066124
## iter 460 value 23.693180
## iter 470 value 23.432414
## iter 480 value 23.227532
## iter 490 value 23.116645
## iter 500 value 23.082550
## final  value 23.082550 
## stopped after 500 iterations
## # weights:  25
## initial  value 1396719.500964 
## iter  10 value 14442.509209
## iter  20 value 8318.877569
## iter  30 value 2449.613836
## iter  40 value 1442.390049
## iter  50 value 1264.974398
## iter  60 value 1229.307221
## iter  70 value 1192.892924
## iter  80 value 1180.448678
## iter  90 value 1176.394756
## iter 100 value 1172.649592
## iter 110 value 1151.043775
## iter 120 value 1139.618268
## iter 130 value 1137.936695
## iter 140 value 1135.762766
## iter 150 value 1135.621121
## iter 160 value 1135.604907
## iter 170 value 1135.571068
## iter 180 value 1135.545407
## final  value 1135.531521 
## converged
## # weights:  61
## initial  value 1395559.158788 
## iter  10 value 2965.362259
## iter  20 value 1648.111629
## iter  30 value 1078.679235
## iter  40 value 858.968683
## iter  50 value 763.990663
## iter  60 value 728.855353
## iter  70 value 688.317954
## iter  80 value 659.053913
## iter  90 value 640.301413
## iter 100 value 622.044528
## iter 110 value 610.951452
## iter 120 value 600.766610
## iter 130 value 595.506993
## iter 140 value 593.075413
## iter 150 value 588.107703
## iter 160 value 579.301858
## iter 170 value 571.500934
## iter 180 value 565.002137
## iter 190 value 563.150493
## iter 200 value 561.290695
## iter 210 value 560.476340
## iter 220 value 560.281806
## iter 230 value 560.173274
## iter 240 value 560.024207
## iter 250 value 559.949920
## iter 260 value 559.567576
## iter 270 value 558.756010
## iter 280 value 558.186569
## iter 290 value 556.313216
## iter 300 value 555.890766
## iter 310 value 555.799140
## iter 320 value 555.623466
## iter 330 value 555.556972
## iter 340 value 555.511387
## iter 350 value 555.476564
## iter 360 value 555.468100
## iter 360 value 555.468096
## iter 360 value 555.468093
## final  value 555.468093 
## converged
## # weights:  121
## initial  value 1367573.323344 
## iter  10 value 1387.922761
## iter  20 value 902.438461
## iter  30 value 741.281949
## iter  40 value 620.089931
## iter  50 value 553.786006
## iter  60 value 507.241444
## iter  70 value 462.427228
## iter  80 value 433.559924
## iter  90 value 413.686746
## iter 100 value 395.764417
## iter 110 value 381.836097
## iter 120 value 374.803124
## iter 130 value 366.707894
## iter 140 value 362.038174
## iter 150 value 355.314352
## iter 160 value 349.264977
## iter 170 value 343.676292
## iter 180 value 337.771236
## iter 190 value 333.605960
## iter 200 value 329.821507
## iter 210 value 324.452005
## iter 220 value 320.154650
## iter 230 value 317.146476
## iter 240 value 315.115750
## iter 250 value 314.159772
## iter 260 value 313.338361
## iter 270 value 312.229241
## iter 280 value 309.663396
## iter 290 value 306.046459
## iter 300 value 302.279587
## iter 310 value 298.687355
## iter 320 value 294.957058
## iter 330 value 291.051472
## iter 340 value 289.002688
## iter 350 value 285.647698
## iter 360 value 282.719438
## iter 370 value 280.046920
## iter 380 value 277.528513
## iter 390 value 274.400966
## iter 400 value 271.914406
## iter 410 value 269.263666
## iter 420 value 266.516046
## iter 430 value 265.142300
## iter 440 value 263.885987
## iter 450 value 262.816387
## iter 460 value 261.020055
## iter 470 value 258.699086
## iter 480 value 255.618241
## iter 490 value 253.725632
## iter 500 value 253.034260
## final  value 253.034260 
## stopped after 500 iterations
## # weights:  181
## initial  value 1376459.602667 
## iter  10 value 1127.345852
## iter  20 value 832.945479
## iter  30 value 674.005026
## iter  40 value 583.374431
## iter  50 value 505.273381
## iter  60 value 469.719277
## iter  70 value 435.532127
## iter  80 value 389.899075
## iter  90 value 352.911699
## iter 100 value 323.992758
## iter 110 value 298.214756
## iter 120 value 274.799992
## iter 130 value 260.649590
## iter 140 value 248.773487
## iter 150 value 230.974150
## iter 160 value 213.545800
## iter 170 value 204.372007
## iter 180 value 198.059819
## iter 190 value 192.314134
## iter 200 value 185.412410
## iter 210 value 177.952262
## iter 220 value 170.755753
## iter 230 value 166.903620
## iter 240 value 163.051089
## iter 250 value 160.017337
## iter 260 value 157.171586
## iter 270 value 154.385850
## iter 280 value 152.290162
## iter 290 value 149.698070
## iter 300 value 147.543353
## iter 310 value 143.980594
## iter 320 value 141.781824
## iter 330 value 139.406858
## iter 340 value 136.832988
## iter 350 value 135.208600
## iter 360 value 134.298948
## iter 370 value 133.566552
## iter 380 value 133.280880
## iter 390 value 132.588687
## iter 400 value 131.662410
## iter 410 value 130.859474
## iter 420 value 129.970304
## iter 430 value 128.990268
## iter 440 value 127.352780
## iter 450 value 126.228489
## iter 460 value 123.407267
## iter 470 value 120.238405
## iter 480 value 118.793030
## iter 490 value 117.143551
## iter 500 value 113.849053
## final  value 113.849053 
## stopped after 500 iterations
## # weights:  241
## initial  value 1389790.278838 
## iter  10 value 1263.565629
## iter  20 value 793.948900
## iter  30 value 632.273659
## iter  40 value 520.049714
## iter  50 value 407.226424
## iter  60 value 347.868354
## iter  70 value 310.508556
## iter  80 value 279.572911
## iter  90 value 246.255188
## iter 100 value 212.968424
## iter 110 value 187.778570
## iter 120 value 169.578705
## iter 130 value 159.193569
## iter 140 value 151.159279
## iter 150 value 142.326458
## iter 160 value 134.003837
## iter 170 value 123.380027
## iter 180 value 116.739004
## iter 190 value 109.334043
## iter 200 value 104.365390
## iter 210 value 100.136316
## iter 220 value 97.064740
## iter 230 value 93.927804
## iter 240 value 88.875125
## iter 250 value 86.337503
## iter 260 value 83.020601
## iter 270 value 80.476937
## iter 280 value 78.064533
## iter 290 value 76.258054
## iter 300 value 74.680371
## iter 310 value 73.122328
## iter 320 value 71.469310
## iter 330 value 69.455467
## iter 340 value 67.549627
## iter 350 value 65.934425
## iter 360 value 64.218492
## iter 370 value 62.305616
## iter 380 value 60.605498
## iter 390 value 59.204247
## iter 400 value 57.526085
## iter 410 value 55.349930
## iter 420 value 53.616819
## iter 430 value 52.351348
## iter 440 value 51.393803
## iter 450 value 50.241243
## iter 460 value 48.883759
## iter 470 value 47.611422
## iter 480 value 46.592423
## iter 490 value 46.342161
## iter 500 value 46.248030
## final  value 46.248030 
## stopped after 500 iterations
## # weights:  25
## initial  value 1386822.515707 
## iter  10 value 15524.497983
## iter  20 value 13787.875760
## iter  30 value 5598.753686
## iter  40 value 4652.935684
## iter  50 value 3910.788261
## iter  60 value 2928.643200
## iter  70 value 2458.895845
## iter  80 value 1769.742705
## iter  90 value 1546.110268
## iter 100 value 1416.175404
## iter 110 value 1301.132530
## iter 120 value 1258.077407
## iter 130 value 1206.200747
## iter 140 value 1157.611149
## iter 150 value 1135.895858
## iter 160 value 1133.170304
## iter 170 value 1132.384037
## iter 180 value 1131.459750
## final  value 1131.435314 
## converged
## # weights:  61
## initial  value 1364693.216837 
## iter  10 value 14839.183375
## iter  20 value 2275.262623
## iter  30 value 1670.628631
## iter  40 value 1321.568565
## iter  50 value 1154.518087
## iter  60 value 1087.910583
## iter  70 value 1018.805567
## iter  80 value 989.827995
## iter  90 value 960.107556
## iter 100 value 928.462403
## iter 110 value 888.292781
## iter 120 value 859.054365
## iter 130 value 846.846802
## iter 140 value 838.287877
## iter 150 value 816.122115
## iter 160 value 792.460452
## iter 170 value 785.912732
## iter 180 value 782.657998
## iter 190 value 780.667595
## iter 200 value 779.275156
## iter 210 value 779.111645
## iter 220 value 779.036366
## iter 230 value 778.989731
## iter 240 value 778.049105
## iter 250 value 772.677945
## iter 260 value 765.026100
## iter 270 value 760.684713
## iter 280 value 760.062316
## iter 290 value 760.053246
## final  value 760.053201 
## converged
## # weights:  121
## initial  value 1380308.695246 
## iter  10 value 1941.494081
## iter  20 value 1195.631407
## iter  30 value 1019.595749
## iter  40 value 885.958336
## iter  50 value 815.992857
## iter  60 value 763.370679
## iter  70 value 732.576310
## iter  80 value 711.444962
## iter  90 value 696.220971
## iter 100 value 677.345606
## iter 110 value 664.857428
## iter 120 value 650.971994
## iter 130 value 639.772116
## iter 140 value 628.478801
## iter 150 value 618.955114
## iter 160 value 610.799521
## iter 170 value 606.893924
## iter 180 value 601.448907
## iter 190 value 594.374696
## iter 200 value 589.413595
## iter 210 value 581.873617
## iter 220 value 576.469197
## iter 230 value 572.721473
## iter 240 value 570.470259
## iter 250 value 569.720771
## iter 260 value 568.911611
## iter 270 value 567.781883
## iter 280 value 566.993064
## iter 290 value 566.179292
## iter 300 value 565.794904
## iter 310 value 565.718524
## iter 320 value 565.697809
## iter 330 value 565.695020
## iter 340 value 565.694307
## iter 340 value 565.694306
## iter 340 value 565.694306
## final  value 565.694306 
## converged
## # weights:  181
## initial  value 1374836.899352 
## iter  10 value 1128.775356
## iter  20 value 844.420908
## iter  30 value 732.954771
## iter  40 value 640.927126
## iter  50 value 587.094388
## iter  60 value 559.877043
## iter  70 value 532.379611
## iter  80 value 510.252722
## iter  90 value 489.555234
## iter 100 value 476.207583
## iter 110 value 463.287706
## iter 120 value 453.160651
## iter 130 value 446.503101
## iter 140 value 440.052510
## iter 150 value 433.779761
## iter 160 value 425.622641
## iter 170 value 414.585996
## iter 180 value 404.975692
## iter 190 value 400.704902
## iter 200 value 398.435012
## iter 210 value 395.758904
## iter 220 value 391.175072
## iter 230 value 386.413329
## iter 240 value 384.734740
## iter 250 value 383.020356
## iter 260 value 380.883195
## iter 270 value 379.378749
## iter 280 value 378.379941
## iter 290 value 377.498714
## iter 300 value 377.017491
## iter 310 value 376.273884
## iter 320 value 374.346056
## iter 330 value 372.433115
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## iter 470 value 366.858327
## iter 480 value 366.302104
## iter 490 value 365.517845
## iter 500 value 365.259951
## final  value 365.259951 
## stopped after 500 iterations
## # weights:  241
## initial  value 1433429.552750 
## iter  10 value 1529.795428
## iter  20 value 965.684454
## iter  30 value 790.941457
## iter  40 value 653.460221
## iter  50 value 585.006296
## iter  60 value 547.056305
## iter  70 value 521.948313
## iter  80 value 501.495627
## iter  90 value 484.720068
## iter 100 value 469.993613
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## iter 320 value 363.922714
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## iter 390 value 353.658001
## iter 400 value 352.003418
## iter 410 value 350.450055
## iter 420 value 349.111709
## iter 430 value 348.219124
## iter 440 value 347.520189
## iter 450 value 346.329727
## iter 460 value 345.171299
## iter 470 value 344.155835
## iter 480 value 343.317025
## iter 490 value 342.881579
## iter 500 value 342.563643
## final  value 342.563643 
## stopped after 500 iterations
## # weights:  25
## initial  value 1386845.223632 
## iter  10 value 6249.655546
## iter  20 value 5381.032915
## iter  30 value 5365.504934
## iter  40 value 5360.341599
## iter  50 value 5316.480930
## iter  60 value 5200.182221
## iter  70 value 4867.016027
## iter  80 value 4245.792816
## iter  90 value 3908.976463
## iter 100 value 3576.700343
## iter 110 value 2962.377398
## iter 120 value 1966.052646
## iter 130 value 1489.568052
## iter 140 value 1394.010661
## iter 150 value 1371.074144
## iter 160 value 1333.086060
## iter 170 value 1276.286310
## iter 180 value 1259.642071
## iter 190 value 1252.414447
## iter 200 value 1249.596952
## iter 210 value 1247.815759
## iter 220 value 1246.132893
## iter 230 value 1245.142563
## iter 240 value 1244.923777
## final  value 1244.923341 
## converged
## # weights:  61
## initial  value 1398990.279805 
## iter  10 value 300265.287882
## iter  20 value 17594.371402
## iter  30 value 10314.928058
## iter  40 value 6158.556648
## iter  50 value 4059.392208
## iter  60 value 2370.793201
## iter  70 value 1318.551290
## iter  80 value 1057.868321
## iter  90 value 960.888073
## iter 100 value 903.440898
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## iter 170 value 777.923118
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## iter 210 value 715.405384
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## iter 230 value 706.752590
## iter 240 value 695.826603
## iter 250 value 693.557235
## iter 260 value 693.403977
## iter 270 value 692.376719
## iter 280 value 689.528007
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## iter 300 value 669.161832
## iter 310 value 666.248076
## iter 320 value 664.874063
## iter 330 value 664.814038
## iter 340 value 664.347113
## iter 350 value 662.075452
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## iter 390 value 657.606759
## iter 400 value 656.442250
## iter 410 value 656.214031
## iter 420 value 653.285933
## iter 430 value 647.437524
## iter 440 value 645.618081
## iter 450 value 645.332349
## iter 460 value 645.314107
## iter 470 value 645.048570
## iter 480 value 644.601991
## iter 490 value 642.344036
## iter 500 value 642.079762
## final  value 642.079762 
## stopped after 500 iterations
## # weights:  121
## initial  value 1294845.404213 
## iter  10 value 3144.129144
## iter  20 value 1372.187645
## iter  30 value 887.961238
## iter  40 value 677.177523
## iter  50 value 589.167057
## iter  60 value 540.391271
## iter  70 value 491.583665
## iter  80 value 462.226517
## iter  90 value 440.941424
## iter 100 value 425.586193
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## iter 210 value 349.081853
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## iter 230 value 338.665130
## iter 240 value 335.571192
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## iter 280 value 329.347168
## iter 290 value 323.855979
## iter 300 value 320.509499
## iter 310 value 317.778994
## iter 320 value 316.307775
## iter 330 value 313.449930
## iter 340 value 306.301199
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## iter 360 value 303.485817
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## iter 390 value 302.992172
## iter 400 value 302.781505
## iter 410 value 302.345765
## iter 420 value 300.224342
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## iter 470 value 295.201915
## iter 480 value 295.101066
## iter 490 value 295.034560
## iter 500 value 294.984937
## final  value 294.984937 
## stopped after 500 iterations
## # weights:  181
## initial  value 1398210.132030 
## iter  10 value 1212.487486
## iter  20 value 790.755675
## iter  30 value 599.559610
## iter  40 value 461.524122
## iter  50 value 376.471882
## iter  60 value 328.070187
## iter  70 value 291.768074
## iter  80 value 251.614135
## iter  90 value 231.770735
## iter 100 value 215.939511
## iter 110 value 204.562011
## iter 120 value 196.291258
## iter 130 value 188.547751
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## iter 150 value 167.869176
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## iter 250 value 141.353163
## iter 260 value 138.726723
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## iter 320 value 128.615059
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## iter 420 value 120.739414
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## iter 460 value 119.071066
## iter 470 value 118.936735
## iter 480 value 118.671012
## iter 490 value 118.378173
## iter 500 value 118.237661
## final  value 118.237661 
## stopped after 500 iterations
## # weights:  241
## initial  value 1459404.078220 
## iter  10 value 1487.519872
## iter  20 value 857.221851
## iter  30 value 644.349686
## iter  40 value 466.824808
## iter  50 value 357.772342
## iter  60 value 301.255913
## iter  70 value 257.157111
## iter  80 value 216.185402
## iter  90 value 191.475789
## iter 100 value 172.037279
## iter 110 value 159.951438
## iter 120 value 150.866382
## iter 130 value 142.374088
## iter 140 value 134.222574
## iter 150 value 126.259450
## iter 160 value 121.928260
## iter 170 value 117.533077
## iter 180 value 113.474639
## iter 190 value 108.756241
## iter 200 value 104.227146
## iter 210 value 100.571784
## iter 220 value 97.222084
## iter 230 value 95.000857
## iter 240 value 92.182776
## iter 250 value 88.722860
## iter 260 value 85.194675
## iter 270 value 82.187523
## iter 280 value 80.285565
## iter 290 value 78.704332
## iter 300 value 77.594670
## iter 310 value 76.698831
## iter 320 value 76.041420
## iter 330 value 75.269978
## iter 340 value 74.662818
## iter 350 value 74.189894
## iter 360 value 73.649097
## iter 370 value 73.076135
## iter 380 value 72.587036
## iter 390 value 71.832190
## iter 400 value 71.344550
## iter 410 value 70.924072
## iter 420 value 70.545764
## iter 430 value 70.178202
## iter 440 value 69.744608
## iter 450 value 69.208610
## iter 460 value 68.752891
## iter 470 value 68.192134
## iter 480 value 67.682656
## iter 490 value 67.453687
## iter 500 value 67.382174
## final  value 67.382174 
## stopped after 500 iterations
## # weights:  25
## initial  value 1348348.565099 
## iter  10 value 16499.206246
## iter  20 value 16499.163274
## iter  20 value 16499.163123
## iter  20 value 16499.163094
## final  value 16499.163094 
## converged
## # weights:  61
## initial  value 1396640.513174 
## iter  10 value 4718.844536
## iter  20 value 3443.463331
## iter  30 value 2533.873000
## iter  40 value 2086.752691
## iter  50 value 1607.773781
## iter  60 value 1101.315221
## iter  70 value 1053.582764
## iter  80 value 1018.449464
## iter  90 value 969.875948
## iter 100 value 891.287658
## iter 110 value 868.836523
## iter 120 value 852.234276
## iter 130 value 843.198578
## iter 140 value 840.460822
## iter 150 value 840.226028
## iter 160 value 838.442078
## iter 170 value 831.110618
## iter 180 value 817.603416
## iter 190 value 813.687248
## iter 200 value 813.233226
## iter 210 value 810.633795
## iter 220 value 808.779332
## iter 230 value 805.719698
## iter 240 value 799.319172
## iter 250 value 797.687126
## iter 260 value 797.667286
## iter 270 value 797.389020
## iter 280 value 795.136071
## iter 290 value 794.254383
## iter 300 value 790.458049
## iter 310 value 781.969177
## iter 320 value 775.534262
## iter 330 value 769.243791
## iter 340 value 766.209801
## iter 350 value 759.672119
## iter 360 value 744.050040
## iter 370 value 733.563384
## iter 380 value 730.662741
## iter 390 value 725.409249
## iter 400 value 711.628712
## iter 410 value 693.799691
## iter 420 value 684.768792
## iter 430 value 676.898720
## iter 440 value 669.788011
## iter 450 value 666.154115
## iter 460 value 665.324632
## iter 470 value 660.951895
## iter 480 value 652.179496
## iter 490 value 648.466928
## iter 500 value 637.004503
## final  value 637.004503 
## stopped after 500 iterations
## # weights:  121
## initial  value 1348256.353507 
## iter  10 value 1481.244996
## iter  20 value 889.570108
## iter  30 value 723.199006
## iter  40 value 642.364368
## iter  50 value 584.535771
## iter  60 value 522.885292
## iter  70 value 469.968395
## iter  80 value 433.619203
## iter  90 value 410.014340
## iter 100 value 388.894309
## iter 110 value 377.225598
## iter 120 value 369.156548
## iter 130 value 363.167629
## iter 140 value 360.212444
## iter 150 value 355.980381
## iter 160 value 352.827071
## iter 170 value 349.369291
## iter 180 value 344.346416
## iter 190 value 338.801184
## iter 200 value 332.202344
## iter 210 value 322.267997
## iter 220 value 317.729843
## iter 230 value 313.875741
## iter 240 value 308.609319
## iter 250 value 307.305798
## iter 260 value 306.896173
## iter 270 value 306.113874
## iter 280 value 305.117725
## iter 290 value 304.310279
## iter 300 value 303.234846
## iter 310 value 299.555035
## iter 320 value 295.164203
## iter 330 value 291.703313
## iter 340 value 289.768803
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## iter 410 value 281.234312
## iter 420 value 279.440954
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## iter 470 value 277.096578
## iter 480 value 276.908288
## iter 490 value 276.720911
## iter 500 value 276.705110
## final  value 276.705110 
## stopped after 500 iterations
## # weights:  181
## initial  value 1445155.038464 
## iter  10 value 1610.565559
## iter  20 value 861.819704
## iter  30 value 609.107140
## iter  40 value 522.382345
## iter  50 value 430.293145
## iter  60 value 338.884494
## iter  70 value 308.797613
## iter  80 value 285.552644
## iter  90 value 271.115860
## iter 100 value 261.662764
## iter 110 value 252.915485
## iter 120 value 245.747651
## iter 130 value 236.054353
## iter 140 value 228.598445
## iter 150 value 221.484267
## iter 160 value 212.437132
## iter 170 value 201.665876
## iter 180 value 193.956356
## iter 190 value 188.465145
## iter 200 value 182.266357
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## iter 220 value 174.843193
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## iter 270 value 166.903332
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## iter 470 value 152.566719
## iter 480 value 151.837237
## iter 490 value 151.122864
## iter 500 value 150.331236
## final  value 150.331236 
## stopped after 500 iterations
## # weights:  241
## initial  value 1409936.924068 
## iter  10 value 1073.645391
## iter  20 value 798.573291
## iter  30 value 673.105183
## iter  40 value 529.072443
## iter  50 value 435.605381
## iter  60 value 364.982799
## iter  70 value 331.076917
## iter  80 value 297.309189
## iter  90 value 255.045402
## iter 100 value 223.517569
## iter 110 value 195.087769
## iter 120 value 175.807606
## iter 130 value 160.921614
## iter 140 value 148.956390
## iter 150 value 140.006274
## iter 160 value 129.097589
## iter 170 value 121.293831
## iter 180 value 106.557323
## iter 190 value 95.712099
## iter 200 value 89.172185
## iter 210 value 85.081968
## iter 220 value 79.018241
## iter 230 value 74.466658
## iter 240 value 70.957451
## iter 250 value 67.435565
## iter 260 value 64.894122
## iter 270 value 62.176107
## iter 280 value 58.528375
## iter 290 value 55.218352
## iter 300 value 52.464610
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## iter 320 value 49.685870
## iter 330 value 47.981879
## iter 340 value 46.050705
## iter 350 value 44.838476
## iter 360 value 43.844011
## iter 370 value 42.633122
## iter 380 value 41.750138
## iter 390 value 41.061854
## iter 400 value 40.602270
## iter 410 value 40.297357
## iter 420 value 40.065167
## iter 430 value 39.772729
## iter 440 value 39.537431
## iter 450 value 39.326951
## iter 460 value 39.107489
## iter 470 value 38.924041
## iter 480 value 38.741348
## iter 490 value 38.654162
## iter 500 value 38.622297
## final  value 38.622297 
## stopped after 500 iterations
## # weights:  25
## initial  value 1373823.380082 
## iter  10 value 6253.279474
## iter  20 value 5725.046452
## iter  30 value 5578.631642
## iter  40 value 5492.007544
## iter  50 value 5490.391503
## iter  60 value 5488.023964
## iter  70 value 5465.954053
## iter  80 value 5396.049880
## iter  90 value 5314.708813
## iter 100 value 5304.316394
## iter 110 value 5291.234272
## iter 120 value 5284.433469
## iter 130 value 5108.555670
## iter 140 value 4410.126345
## iter 150 value 3051.899041
## iter 160 value 1695.725783
## iter 170 value 1385.321981
## iter 180 value 1333.434226
## iter 190 value 1322.665567
## iter 200 value 1293.129959
## iter 210 value 1285.278635
## iter 220 value 1280.491969
## iter 230 value 1278.063909
## iter 240 value 1277.371212
## iter 250 value 1277.366433
## final  value 1277.366333 
## converged
## # weights:  61
## initial  value 1364398.873508 
## iter  10 value 11905.873694
## iter  20 value 2667.673914
## iter  30 value 2344.956231
## iter  40 value 2182.427176
## iter  50 value 1877.229347
## iter  60 value 1543.205346
## iter  70 value 1329.184053
## iter  80 value 1207.074647
## iter  90 value 1013.733243
## iter 100 value 839.146596
## iter 110 value 801.211787
## iter 120 value 788.557229
## iter 130 value 784.419599
## iter 140 value 782.220552
## iter 150 value 780.065125
## iter 160 value 776.590726
## iter 170 value 775.864267
## iter 180 value 775.730755
## iter 190 value 775.447128
## iter 200 value 775.002045
## iter 210 value 773.817949
## iter 220 value 772.707608
## iter 230 value 770.431204
## iter 240 value 768.420010
## iter 250 value 767.649977
## iter 260 value 767.246021
## iter 270 value 767.157348
## iter 280 value 767.093022
## iter 290 value 767.062501
## iter 300 value 767.059870
## iter 310 value 767.044548
## iter 320 value 766.981909
## iter 330 value 766.816382
## iter 340 value 766.745742
## iter 350 value 766.688524
## iter 360 value 766.675249
## iter 370 value 766.656865
## iter 380 value 766.650118
## iter 390 value 766.642445
## iter 400 value 766.636582
## iter 410 value 766.414065
## iter 420 value 766.353365
## iter 430 value 764.901316
## iter 440 value 760.671367
## iter 450 value 757.236755
## iter 460 value 754.824711
## iter 470 value 754.778566
## iter 480 value 754.637851
## iter 490 value 754.514668
## iter 500 value 754.444447
## final  value 754.444447 
## stopped after 500 iterations
## # weights:  121
## initial  value 1356508.019062 
## iter  10 value 1253.270288
## iter  20 value 913.879343
## iter  30 value 736.663029
## iter  40 value 625.605150
## iter  50 value 570.936527
## iter  60 value 515.505339
## iter  70 value 475.469986
## iter  80 value 450.866247
## iter  90 value 427.615815
## iter 100 value 407.348094
## iter 110 value 392.113625
## iter 120 value 376.819981
## iter 130 value 354.395361
## iter 140 value 334.869376
## iter 150 value 320.676336
## iter 160 value 314.852385
## iter 170 value 305.793682
## iter 180 value 303.219220
## iter 190 value 299.118338
## iter 200 value 296.190676
## iter 210 value 293.661481
## iter 220 value 291.709997
## iter 230 value 288.193357
## iter 240 value 283.146954
## iter 250 value 281.467353
## iter 260 value 280.642471
## iter 270 value 279.772466
## iter 280 value 278.880780
## iter 290 value 277.171569
## iter 300 value 273.827177
## iter 310 value 268.627273
## iter 320 value 263.498491
## iter 330 value 258.927127
## iter 340 value 255.650731
## iter 350 value 253.147664
## iter 360 value 250.833061
## iter 370 value 249.681736
## iter 380 value 249.417837
## iter 390 value 249.362799
## iter 400 value 249.292249
## iter 410 value 249.264165
## iter 420 value 249.253116
## iter 430 value 249.242019
## iter 440 value 249.207141
## iter 450 value 248.832596
## iter 460 value 248.237593
## iter 470 value 248.035217
## iter 480 value 247.998048
## iter 490 value 247.987791
## iter 500 value 247.987450
## final  value 247.987450 
## stopped after 500 iterations
## # weights:  181
## initial  value 1336740.011918 
## iter  10 value 1181.090906
## iter  20 value 759.032193
## iter  30 value 605.281300
## iter  40 value 496.083022
## iter  50 value 439.235673
## iter  60 value 376.746852
## iter  70 value 326.064156
## iter  80 value 286.730812
## iter  90 value 253.279918
## iter 100 value 235.067294
## iter 110 value 225.668360
## iter 120 value 210.918935
## iter 130 value 197.039322
## iter 140 value 182.861891
## iter 150 value 162.580669
## iter 160 value 145.444799
## iter 170 value 136.020147
## iter 180 value 129.499577
## iter 190 value 121.993016
## iter 200 value 117.112954
## iter 210 value 114.062543
## iter 220 value 111.870154
## iter 230 value 109.264849
## iter 240 value 106.984345
## iter 250 value 104.917347
## iter 260 value 102.345439
## iter 270 value 99.048368
## iter 280 value 96.514968
## iter 290 value 94.555239
## iter 300 value 93.003141
## iter 310 value 91.726377
## iter 320 value 90.713337
## iter 330 value 89.694674
## iter 340 value 88.251471
## iter 350 value 87.158091
## iter 360 value 86.394645
## iter 370 value 85.876634
## iter 380 value 85.704299
## iter 390 value 85.410861
## iter 400 value 85.140944
## iter 410 value 84.884743
## iter 420 value 84.596774
## iter 430 value 84.338620
## iter 440 value 84.009583
## iter 450 value 83.689817
## iter 460 value 83.261688
## iter 470 value 82.608659
## iter 480 value 81.882700
## iter 490 value 81.511698
## iter 500 value 81.218299
## final  value 81.218299 
## stopped after 500 iterations
## # weights:  241
## initial  value 1399188.347897 
## iter  10 value 1409.625308
## iter  20 value 795.392802
## iter  30 value 662.767686
## iter  40 value 547.238388
## iter  50 value 434.876154
## iter  60 value 345.910558
## iter  70 value 298.936100
## iter  80 value 262.613598
## iter  90 value 230.207643
## iter 100 value 206.227783
## iter 110 value 184.471926
## iter 120 value 169.644918
## iter 130 value 161.393077
## iter 140 value 155.801446
## iter 150 value 149.226867
## iter 160 value 139.754978
## iter 170 value 128.640763
## iter 180 value 121.980477
## iter 190 value 117.368666
## iter 200 value 113.337688
## iter 210 value 106.947228
## iter 220 value 101.278063
## iter 230 value 98.297525
## iter 240 value 95.825437
## iter 250 value 93.467519
## iter 260 value 90.223053
## iter 270 value 83.331872
## iter 280 value 78.570581
## iter 290 value 72.948142
## iter 300 value 69.711889
## iter 310 value 64.352941
## iter 320 value 59.498035
## iter 330 value 55.835749
## iter 340 value 53.390554
## iter 350 value 50.640284
## iter 360 value 49.077468
## iter 370 value 47.972969
## iter 380 value 47.192108
## iter 390 value 46.430641
## iter 400 value 45.792278
## iter 410 value 45.137261
## iter 420 value 44.236903
## iter 430 value 43.587327
## iter 440 value 43.209195
## iter 450 value 42.941393
## iter 460 value 42.636025
## iter 470 value 42.363021
## iter 480 value 42.018238
## iter 490 value 41.560420
## iter 500 value 41.417481
## final  value 41.417481 
## stopped after 500 iterations
## # weights:  25
## initial  value 1403786.083865 
## iter  10 value 109090.575854
## iter  20 value 12235.275131
## iter  30 value 10458.293873
## iter  40 value 7729.915632
## iter  50 value 7098.728154
## iter  60 value 6936.036218
## iter  70 value 6845.601719
## iter  80 value 6831.785691
## iter  90 value 5306.701518
## iter 100 value 5273.013103
## iter 110 value 5161.193596
## iter 120 value 5115.887293
## iter 130 value 5036.311116
## iter 140 value 4924.146262
## iter 150 value 4875.879097
## iter 160 value 4874.923199
## iter 170 value 4874.905242
## iter 180 value 4874.893180
## iter 190 value 4874.847029
## iter 200 value 4874.809686
## iter 210 value 4874.799255
## iter 220 value 4851.453366
## iter 230 value 4849.099113
## iter 240 value 4846.060725
## iter 250 value 4843.024676
## iter 260 value 4842.887685
## final  value 4842.871187 
## converged
## # weights:  61
## initial  value 1393088.021461 
## iter  10 value 3260.795846
## iter  20 value 1384.820038
## iter  30 value 1155.267180
## iter  40 value 994.360390
## iter  50 value 869.922356
## iter  60 value 785.870009
## iter  70 value 731.120828
## iter  80 value 678.912984
## iter  90 value 659.770267
## iter 100 value 649.324934
## iter 110 value 643.098544
## iter 120 value 639.799592
## iter 130 value 639.135584
## iter 140 value 638.384518
## iter 150 value 636.736730
## iter 160 value 633.972176
## iter 170 value 628.206410
## iter 180 value 616.282653
## iter 190 value 595.848528
## iter 200 value 569.711816
## iter 210 value 553.979811
## iter 220 value 545.787447
## iter 230 value 534.945899
## iter 240 value 527.557535
## iter 250 value 525.554159
## iter 260 value 525.277297
## iter 270 value 523.367951
## iter 280 value 521.871232
## iter 290 value 520.658230
## iter 300 value 520.458818
## iter 310 value 518.787633
## iter 320 value 512.649611
## iter 330 value 512.040457
## iter 340 value 511.492133
## iter 350 value 509.990577
## iter 360 value 508.364601
## iter 370 value 508.250028
## iter 380 value 508.247649
## iter 390 value 508.231024
## iter 400 value 508.211727
## iter 410 value 508.064736
## iter 420 value 507.918188
## iter 430 value 507.804248
## iter 440 value 507.720711
## iter 450 value 507.574598
## iter 460 value 507.561893
## iter 470 value 507.561175
## iter 480 value 507.558771
## iter 490 value 507.550793
## final  value 507.550416 
## converged
## # weights:  121
## initial  value 1412465.598450 
## iter  10 value 3644.689711
## iter  20 value 1284.277922
## iter  30 value 927.686067
## iter  40 value 858.829070
## iter  50 value 826.437010
## iter  60 value 792.697973
## iter  70 value 734.664321
## iter  80 value 705.302068
## iter  90 value 669.431413
## iter 100 value 615.021180
## iter 110 value 585.162706
## iter 120 value 561.139588
## iter 130 value 531.179863
## iter 140 value 514.026613
## iter 150 value 504.161953
## iter 160 value 490.860952
## iter 170 value 482.631429
## iter 180 value 481.610414
## iter 190 value 479.876922
## iter 200 value 477.322793
## iter 210 value 476.408946
## iter 220 value 476.174850
## iter 230 value 475.395736
## iter 240 value 474.708554
## iter 250 value 473.786295
## iter 260 value 473.523383
## iter 270 value 473.304851
## iter 280 value 472.774154
## iter 290 value 472.398132
## iter 300 value 471.855370
## iter 310 value 470.562349
## iter 320 value 469.715299
## iter 330 value 469.553279
## iter 340 value 469.370841
## iter 350 value 468.942093
## iter 360 value 468.300864
## iter 370 value 468.043216
## iter 380 value 467.725542
## iter 390 value 467.626573
## iter 400 value 467.561666
## iter 410 value 467.513369
## iter 420 value 467.451402
## iter 430 value 467.431944
## iter 440 value 467.405126
## iter 450 value 467.400114
## iter 460 value 467.398315
## final  value 467.397823 
## converged
## # weights:  181
## initial  value 1378212.034278 
## iter  10 value 1703.541153
## iter  20 value 1101.212112
## iter  30 value 731.917070
## iter  40 value 568.798338
## iter  50 value 462.899902
## iter  60 value 398.317097
## iter  70 value 360.462422
## iter  80 value 327.909791
## iter  90 value 304.697749
## iter 100 value 282.805433
## iter 110 value 262.578836
## iter 120 value 245.889765
## iter 130 value 229.466097
## iter 140 value 215.270058
## iter 150 value 207.713023
## iter 160 value 200.510845
## iter 170 value 195.583108
## iter 180 value 192.284509
## iter 190 value 188.651419
## iter 200 value 185.605565
## iter 210 value 183.961976
## iter 220 value 182.321087
## iter 230 value 181.391753
## iter 240 value 180.220902
## iter 250 value 178.327857
## iter 260 value 176.647529
## iter 270 value 173.243753
## iter 280 value 170.589587
## iter 290 value 168.088739
## iter 300 value 165.664329
## iter 310 value 164.400616
## iter 320 value 163.630390
## iter 330 value 162.671111
## iter 340 value 161.375750
## iter 350 value 159.708574
## iter 360 value 158.172092
## iter 370 value 157.059211
## iter 380 value 156.773046
## iter 390 value 156.177545
## iter 400 value 154.012173
## iter 410 value 150.051945
## iter 420 value 143.356092
## iter 430 value 139.358932
## iter 440 value 137.001456
## iter 450 value 135.145501
## iter 460 value 133.658801
## iter 470 value 132.715739
## iter 480 value 131.910983
## iter 490 value 131.344263
## iter 500 value 130.791752
## final  value 130.791752 
## stopped after 500 iterations
## # weights:  241
## initial  value 1414928.952290 
## iter  10 value 1112.083537
## iter  20 value 820.314474
## iter  30 value 627.938497
## iter  40 value 471.243433
## iter  50 value 352.983889
## iter  60 value 304.959582
## iter  70 value 263.290683
## iter  80 value 200.116239
## iter  90 value 156.462294
## iter 100 value 126.919666
## iter 110 value 108.706463
## iter 120 value 99.617129
## iter 130 value 92.514948
## iter 140 value 86.640786
## iter 150 value 80.254759
## iter 160 value 74.218510
## iter 170 value 68.952178
## iter 180 value 65.317075
## iter 190 value 62.231048
## iter 200 value 59.476403
## iter 210 value 56.405730
## iter 220 value 52.293064
## iter 230 value 47.774677
## iter 240 value 45.708373
## iter 250 value 43.545619
## iter 260 value 41.375606
## iter 270 value 38.989487
## iter 280 value 36.060379
## iter 290 value 34.187959
## iter 300 value 32.880289
## iter 310 value 31.892459
## iter 320 value 31.052977
## iter 330 value 30.432835
## iter 340 value 29.467565
## iter 350 value 28.509170
## iter 360 value 27.523931
## iter 370 value 26.181882
## iter 380 value 25.403279
## iter 390 value 24.759019
## iter 400 value 24.351240
## iter 410 value 24.029185
## iter 420 value 23.818087
## iter 430 value 23.649284
## iter 440 value 23.488856
## iter 450 value 23.266357
## iter 460 value 22.540332
## iter 470 value 22.050322
## iter 480 value 21.749350
## iter 490 value 21.343178
## iter 500 value 21.085269
## final  value 21.085269 
## stopped after 500 iterations
## # weights:  25
## initial  value 1403838.960057 
## iter  10 value 506824.749182
## iter  20 value 44386.175785
## iter  30 value 15890.689843
## iter  40 value 9416.351899
## iter  50 value 8490.905770
## iter  60 value 5735.553237
## iter  70 value 5010.229939
## iter  80 value 4047.326041
## iter  90 value 3766.492297
## iter 100 value 2522.448204
## iter 110 value 1765.843199
## iter 120 value 1333.882634
## iter 130 value 1237.414213
## iter 140 value 1229.070896
## iter 150 value 1200.979018
## iter 160 value 1175.562164
## iter 170 value 1171.947238
## iter 180 value 1171.382560
## iter 190 value 1171.379816
## iter 190 value 1171.379813
## iter 200 value 1171.378623
## final  value 1171.378169 
## converged
## # weights:  61
## initial  value 1355729.880341 
## iter  10 value 10900.552477
## iter  20 value 6255.930602
## iter  30 value 4978.361437
## iter  40 value 4354.622460
## iter  50 value 3347.467992
## iter  60 value 2270.692950
## iter  70 value 1939.407852
## iter  80 value 1809.152073
## iter  90 value 1627.059129
## iter 100 value 1495.984986
## iter 110 value 1459.819661
## iter 120 value 1435.237228
## iter 130 value 1424.999084
## iter 140 value 1396.703192
## iter 150 value 1245.071424
## iter 160 value 1164.136867
## iter 170 value 1120.667877
## iter 180 value 1077.657534
## iter 190 value 1062.230791
## iter 200 value 1050.255460
## iter 210 value 1024.342159
## iter 220 value 1010.332015
## iter 230 value 988.807795
## iter 240 value 966.806886
## iter 250 value 945.875646
## iter 260 value 941.171965
## iter 270 value 936.601313
## iter 280 value 928.771873
## iter 290 value 924.891796
## iter 300 value 920.281473
## iter 310 value 917.349135
## iter 320 value 917.024758
## iter 330 value 916.934474
## iter 340 value 916.928242
## final  value 916.928060 
## converged
## # weights:  121
## initial  value 1403073.843998 
## iter  10 value 2107.523782
## iter  20 value 1362.300897
## iter  30 value 1185.796867
## iter  40 value 1048.747753
## iter  50 value 931.796074
## iter  60 value 855.519058
## iter  70 value 808.389906
## iter  80 value 782.364516
## iter  90 value 767.325278
## iter 100 value 755.388188
## iter 110 value 742.132719
## iter 120 value 726.371402
## iter 130 value 712.753263
## iter 140 value 683.452858
## iter 150 value 665.893149
## iter 160 value 650.454591
## iter 170 value 633.277032
## iter 180 value 612.447425
## iter 190 value 606.462573
## iter 200 value 600.277575
## iter 210 value 595.455302
## iter 220 value 592.182436
## iter 230 value 589.140085
## iter 240 value 586.357115
## iter 250 value 585.349842
## iter 260 value 584.342387
## iter 270 value 581.812184
## iter 280 value 580.365301
## iter 290 value 579.592517
## iter 300 value 579.106209
## iter 310 value 578.146926
## iter 320 value 577.544223
## iter 330 value 576.387130
## iter 340 value 575.632030
## iter 350 value 575.360008
## iter 360 value 575.273223
## iter 370 value 575.252466
## iter 380 value 575.246704
## final  value 575.246490 
## converged
## # weights:  181
## initial  value 1392525.760123 
## iter  10 value 1335.243737
## iter  20 value 937.276248
## iter  30 value 741.748519
## iter  40 value 637.818077
## iter  50 value 567.992999
## iter  60 value 517.702860
## iter  70 value 483.021601
## iter  80 value 464.102084
## iter  90 value 446.484026
## iter 100 value 430.887759
## iter 110 value 418.062586
## iter 120 value 408.244518
## iter 130 value 402.325369
## iter 140 value 397.906754
## iter 150 value 394.107682
## iter 160 value 386.600080
## iter 170 value 380.389622
## iter 180 value 375.817339
## iter 190 value 372.784996
## iter 200 value 370.527116
## iter 210 value 368.812003
## iter 220 value 367.364170
## iter 230 value 366.109081
## iter 240 value 362.394829
## iter 250 value 360.921792
## iter 260 value 360.240145
## iter 270 value 359.624404
## iter 280 value 359.210443
## iter 290 value 358.341089
## iter 300 value 355.190865
## iter 310 value 352.535360
## iter 320 value 351.164719
## iter 330 value 349.681241
## iter 340 value 348.278906
## iter 350 value 347.553564
## iter 360 value 346.485252
## iter 370 value 345.802900
## iter 380 value 345.348713
## iter 390 value 344.545281
## iter 400 value 343.145703
## iter 410 value 342.028837
## iter 420 value 340.858727
## iter 430 value 339.902367
## iter 440 value 339.284692
## iter 450 value 338.800417
## iter 460 value 338.495463
## iter 470 value 338.284241
## iter 480 value 338.161969
## iter 490 value 338.108534
## iter 500 value 338.072148
## final  value 338.072148 
## stopped after 500 iterations
## # weights:  241
## initial  value 1389319.093287 
## iter  10 value 1355.486815
## iter  20 value 1000.114558
## iter  30 value 805.397807
## iter  40 value 709.435649
## iter  50 value 630.817504
## iter  60 value 577.716356
## iter  70 value 548.936664
## iter  80 value 527.646623
## iter  90 value 501.442998
## iter 100 value 468.056672
## iter 110 value 446.169373
## iter 120 value 430.342575
## iter 130 value 417.665335
## iter 140 value 406.531938
## iter 150 value 397.384910
## iter 160 value 391.721459
## iter 170 value 387.764683
## iter 180 value 383.722419
## iter 190 value 377.288687
## iter 200 value 371.808477
## iter 210 value 367.296776
## iter 220 value 362.981632
## iter 230 value 359.147544
## iter 240 value 354.019349
## iter 250 value 349.746836
## iter 260 value 345.669764
## iter 270 value 341.404619
## iter 280 value 338.552120
## iter 290 value 336.702187
## iter 300 value 335.357523
## iter 310 value 333.603358
## iter 320 value 331.783614
## iter 330 value 329.627795
## iter 340 value 327.266060
## iter 350 value 325.054886
## iter 360 value 323.434266
## iter 370 value 321.904966
## iter 380 value 320.446616
## iter 390 value 318.934533
## iter 400 value 317.335452
## iter 410 value 316.120889
## iter 420 value 314.927469
## iter 430 value 313.609442
## iter 440 value 312.638502
## iter 450 value 311.887785
## iter 460 value 311.300014
## iter 470 value 310.848868
## iter 480 value 310.418241
## iter 490 value 310.237849
## iter 500 value 310.019176
## final  value 310.019176 
## stopped after 500 iterations
## # weights:  25
## initial  value 1416069.583389 
## iter  10 value 2238.202111
## iter  20 value 1738.803993
## iter  30 value 1284.351022
## iter  40 value 1131.389143
## iter  50 value 1081.814527
## iter  60 value 1061.382319
## iter  70 value 1048.601237
## iter  80 value 990.430776
## iter  90 value 972.012073
## iter 100 value 967.280400
## iter 110 value 966.332469
## iter 120 value 965.780017
## iter 130 value 959.377795
## iter 140 value 956.241841
## iter 150 value 954.355821
## iter 160 value 954.296917
## iter 170 value 953.246912
## iter 180 value 952.741974
## iter 190 value 952.704013
## iter 200 value 949.971875
## iter 210 value 949.008291
## iter 220 value 948.944734
## iter 230 value 947.015530
## iter 240 value 946.247923
## iter 250 value 945.320073
## iter 260 value 944.904092
## iter 270 value 944.886916
## iter 280 value 944.499011
## iter 290 value 944.033353
## iter 300 value 943.661728
## iter 310 value 943.579053
## iter 320 value 943.577984
## iter 330 value 943.441042
## iter 340 value 943.422131
## iter 350 value 943.418132
## final  value 943.417967 
## converged
## # weights:  61
## initial  value 1365216.099806 
## iter  10 value 6421.776890
## iter  20 value 2312.000363
## iter  30 value 1819.398583
## iter  40 value 1502.520580
## iter  50 value 1383.553475
## iter  60 value 1278.944868
## iter  70 value 1207.198119
## iter  80 value 1161.079270
## iter  90 value 1153.772345
## iter 100 value 1114.059747
## iter 110 value 1062.468245
## iter 120 value 1001.220238
## iter 130 value 994.606825
## iter 140 value 992.781361
## iter 150 value 991.429252
## iter 160 value 989.304512
## iter 170 value 987.049758
## iter 180 value 983.614944
## iter 190 value 981.419372
## iter 200 value 976.255426
## iter 210 value 969.667827
## iter 220 value 963.887997
## iter 230 value 938.604985
## iter 240 value 901.043385
## iter 250 value 873.868881
## iter 260 value 822.605610
## iter 270 value 772.383496
## iter 280 value 740.002720
## iter 290 value 735.719149
## iter 300 value 732.670863
## iter 310 value 731.429551
## iter 320 value 730.310025
## iter 330 value 730.305342
## iter 340 value 730.302079
## final  value 730.301941 
## converged
## # weights:  121
## initial  value 1403325.763243 
## iter  10 value 1122.487686
## iter  20 value 870.227515
## iter  30 value 727.204114
## iter  40 value 619.104864
## iter  50 value 578.628219
## iter  60 value 521.944199
## iter  70 value 480.942835
## iter  80 value 435.703695
## iter  90 value 415.579434
## iter 100 value 399.878136
## iter 110 value 383.483034
## iter 120 value 366.345877
## iter 130 value 353.867708
## iter 140 value 346.336705
## iter 150 value 340.493317
## iter 160 value 338.023993
## iter 170 value 336.001899
## iter 180 value 328.304142
## iter 190 value 314.658477
## iter 200 value 307.344009
## iter 210 value 303.593217
## iter 220 value 300.228981
## iter 230 value 297.636419
## iter 240 value 294.041175
## iter 250 value 291.364961
## iter 260 value 288.737225
## iter 270 value 286.071399
## iter 280 value 281.079543
## iter 290 value 277.858500
## iter 300 value 273.132394
## iter 310 value 267.807826
## iter 320 value 263.389189
## iter 330 value 261.578534
## iter 340 value 259.530114
## iter 350 value 256.177391
## iter 360 value 254.380968
## iter 370 value 252.597780
## iter 380 value 250.710564
## iter 390 value 249.421687
## iter 400 value 248.534595
## iter 410 value 248.066951
## iter 420 value 247.662992
## iter 430 value 246.979507
## iter 440 value 246.025056
## iter 450 value 245.623730
## iter 460 value 245.473853
## iter 470 value 245.422949
## iter 480 value 245.414909
## iter 490 value 245.408678
## iter 500 value 245.407584
## final  value 245.407584 
## stopped after 500 iterations
## # weights:  181
## initial  value 1361206.670699 
## iter  10 value 1295.858029
## iter  20 value 828.104091
## iter  30 value 640.038740
## iter  40 value 509.873449
## iter  50 value 418.032066
## iter  60 value 364.276351
## iter  70 value 305.188981
## iter  80 value 270.269344
## iter  90 value 242.275767
## iter 100 value 226.337498
## iter 110 value 214.348055
## iter 120 value 203.248665
## iter 130 value 196.357158
## iter 140 value 192.667514
## iter 150 value 188.640037
## iter 160 value 185.457041
## iter 170 value 182.851720
## iter 180 value 180.653876
## iter 190 value 177.588265
## iter 200 value 174.258912
## iter 210 value 169.930911
## iter 220 value 167.074952
## iter 230 value 166.433534
## iter 240 value 165.095074
## iter 250 value 163.829953
## iter 260 value 161.979264
## iter 270 value 160.882454
## iter 280 value 160.018837
## iter 290 value 158.952691
## iter 300 value 157.805368
## iter 310 value 156.446891
## iter 320 value 154.915213
## iter 330 value 152.030485
## iter 340 value 149.456178
## iter 350 value 147.074628
## iter 360 value 146.014718
## iter 370 value 145.682585
## iter 380 value 145.483806
## iter 390 value 145.313174
## iter 400 value 145.157224
## iter 410 value 143.826943
## iter 420 value 143.375155
## iter 430 value 143.035746
## iter 440 value 141.406108
## iter 450 value 138.713330
## iter 460 value 136.786968
## iter 470 value 134.950843
## iter 480 value 133.732260
## iter 490 value 132.706544
## iter 500 value 132.037020
## final  value 132.037020 
## stopped after 500 iterations
## # weights:  241
## initial  value 1354588.740780 
## iter  10 value 1082.956447
## iter  20 value 749.391734
## iter  30 value 612.049103
## iter  40 value 468.374614
## iter  50 value 360.245307
## iter  60 value 296.264092
## iter  70 value 263.289819
## iter  80 value 218.002100
## iter  90 value 184.676417
## iter 100 value 153.140980
## iter 110 value 125.636393
## iter 120 value 103.308894
## iter 130 value 90.000521
## iter 140 value 82.016647
## iter 150 value 76.519829
## iter 160 value 71.715830
## iter 170 value 66.620242
## iter 180 value 61.051333
## iter 190 value 56.394968
## iter 200 value 53.587763
## iter 210 value 50.891358
## iter 220 value 49.009867
## iter 230 value 47.600313
## iter 240 value 46.454343
## iter 250 value 45.080640
## iter 260 value 43.992991
## iter 270 value 42.643544
## iter 280 value 41.306902
## iter 290 value 40.335477
## iter 300 value 39.757244
## iter 310 value 39.117204
## iter 320 value 38.510405
## iter 330 value 37.651777
## iter 340 value 36.855159
## iter 350 value 36.087352
## iter 360 value 35.567927
## iter 370 value 35.228022
## iter 380 value 34.884248
## iter 390 value 34.511737
## iter 400 value 33.995170
## iter 410 value 33.054014
## iter 420 value 32.118513
## iter 430 value 31.499032
## iter 440 value 31.066530
## iter 450 value 30.775875
## iter 460 value 30.589733
## iter 470 value 30.410489
## iter 480 value 30.152226
## iter 490 value 30.049611
## iter 500 value 29.988686
## final  value 29.988686 
## stopped after 500 iterations
## # weights:  25
## initial  value 1433122.288894 
## iter  10 value 9355.237341
## iter  20 value 3640.491481
## iter  30 value 1805.757171
## iter  40 value 1470.570940
## iter  50 value 1403.186617
## iter  60 value 1364.137259
## iter  70 value 1272.479221
## iter  80 value 1263.912584
## iter  90 value 1262.800666
## iter 100 value 1262.531084
## iter 110 value 1258.487066
## iter 120 value 1255.504697
## iter 130 value 1253.804049
## iter 140 value 1253.512088
## iter 150 value 1253.091456
## iter 160 value 1252.552893
## iter 170 value 1251.709633
## iter 180 value 1240.958941
## iter 190 value 1237.037129
## iter 200 value 1236.678964
## iter 210 value 1236.206232
## iter 220 value 1236.187591
## iter 230 value 1235.936937
## iter 240 value 1235.795204
## iter 250 value 1235.176899
## iter 260 value 1235.056227
## iter 270 value 1235.048436
## iter 280 value 1235.007871
## iter 290 value 1234.931889
## iter 300 value 1234.865749
## iter 300 value 1234.865747
## final  value 1234.865640 
## converged
## # weights:  61
## initial  value 1409890.577075 
## iter  10 value 57180.463238
## iter  20 value 19955.574565
## iter  30 value 15381.741605
## iter  40 value 9944.453119
## iter  50 value 6119.117484
## iter  60 value 3519.940325
## iter  70 value 1719.690705
## iter  80 value 1164.410276
## iter  90 value 1049.969774
## iter 100 value 943.399915
## iter 110 value 890.761131
## iter 120 value 859.182661
## iter 130 value 854.912781
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## iter 150 value 849.802608
## iter 160 value 835.907639
## iter 170 value 816.333326
## iter 180 value 804.644243
## iter 190 value 796.343339
## iter 200 value 780.249106
## iter 210 value 767.763435
## iter 220 value 757.905823
## iter 230 value 754.430889
## iter 240 value 746.911441
## iter 250 value 743.980767
## iter 260 value 743.772738
## iter 270 value 743.202272
## iter 280 value 742.293783
## iter 290 value 740.824125
## iter 300 value 739.595650
## iter 310 value 738.275292
## iter 320 value 737.634340
## iter 330 value 736.528374
## iter 340 value 735.798473
## iter 350 value 735.537174
## iter 360 value 735.086464
## iter 370 value 733.618209
## iter 380 value 733.280630
## iter 390 value 733.150795
## iter 400 value 732.861152
## iter 410 value 732.427658
## iter 420 value 729.692731
## iter 430 value 726.548475
## iter 440 value 721.240876
## iter 450 value 717.506936
## iter 460 value 716.112585
## iter 470 value 715.440512
## iter 480 value 715.150605
## iter 490 value 714.246981
## iter 500 value 713.665812
## final  value 713.665812 
## stopped after 500 iterations
## # weights:  121
## initial  value 1435570.100722 
## iter  10 value 161003.576662
## iter  20 value 9210.730501
## iter  30 value 5644.791893
## iter  40 value 5100.405213
## iter  50 value 4184.659583
## iter  60 value 3372.636716
## iter  70 value 2611.047442
## iter  80 value 2153.274016
## iter  90 value 1660.864843
## iter 100 value 1261.213830
## iter 110 value 952.094075
## iter 120 value 899.364230
## iter 130 value 862.149083
## iter 140 value 836.689643
## iter 150 value 824.529595
## iter 160 value 819.746219
## iter 170 value 806.845982
## iter 180 value 800.717655
## iter 190 value 791.382667
## iter 200 value 789.265642
## iter 210 value 783.871317
## iter 220 value 773.693582
## iter 230 value 761.720787
## iter 240 value 755.247007
## iter 250 value 749.786105
## iter 260 value 733.062404
## iter 270 value 709.629572
## iter 280 value 702.718498
## iter 290 value 701.980737
## iter 300 value 695.450951
## iter 310 value 691.175581
## iter 320 value 679.606961
## iter 330 value 668.683713
## iter 340 value 659.455739
## iter 350 value 654.392772
## iter 360 value 652.260823
## iter 370 value 650.818736
## iter 380 value 647.731048
## iter 390 value 640.141321
## iter 400 value 627.817770
## iter 410 value 615.694426
## iter 420 value 609.029886
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## iter 440 value 601.425851
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## iter 460 value 598.354456
## iter 470 value 589.610583
## iter 480 value 574.158178
## iter 490 value 565.574964
## iter 500 value 543.570045
## final  value 543.570045 
## stopped after 500 iterations
## # weights:  181
## initial  value 1393214.604692 
## iter  10 value 1044.378032
## iter  20 value 833.073995
## iter  30 value 693.406663
## iter  40 value 540.787301
## iter  50 value 431.685996
## iter  60 value 364.670579
## iter  70 value 320.152970
## iter  80 value 278.274023
## iter  90 value 248.736476
## iter 100 value 225.284833
## iter 110 value 212.190219
## iter 120 value 201.809786
## iter 130 value 193.461049
## iter 140 value 184.016035
## iter 150 value 173.470433
## iter 160 value 163.409213
## iter 170 value 153.234069
## iter 180 value 146.096020
## iter 190 value 141.746530
## iter 200 value 136.417572
## iter 210 value 132.422299
## iter 220 value 129.073834
## iter 230 value 127.036761
## iter 240 value 125.936890
## iter 250 value 124.534620
## iter 260 value 123.106331
## iter 270 value 122.017066
## iter 280 value 121.425048
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## iter 300 value 117.692930
## iter 310 value 115.661008
## iter 320 value 114.833395
## iter 330 value 113.811333
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## iter 390 value 110.931430
## iter 400 value 110.737485
## iter 410 value 110.418591
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## iter 440 value 109.605522
## iter 450 value 109.418351
## iter 460 value 109.028662
## iter 470 value 108.095283
## iter 480 value 107.550481
## iter 490 value 107.257003
## iter 500 value 106.846898
## final  value 106.846898 
## stopped after 500 iterations
## # weights:  241
## initial  value 1375678.698071 
## iter  10 value 1633.474674
## iter  20 value 853.425435
## iter  30 value 677.816412
## iter  40 value 555.245483
## iter  50 value 447.420550
## iter  60 value 379.860769
## iter  70 value 337.310393
## iter  80 value 300.906447
## iter  90 value 267.619889
## iter 100 value 230.193146
## iter 110 value 194.239187
## iter 120 value 171.994263
## iter 130 value 156.149686
## iter 140 value 145.085650
## iter 150 value 132.480395
## iter 160 value 122.601386
## iter 170 value 117.600712
## iter 180 value 112.173946
## iter 190 value 105.293102
## iter 200 value 98.212996
## iter 210 value 93.171072
## iter 220 value 86.522761
## iter 230 value 81.169382
## iter 240 value 74.650970
## iter 250 value 70.013466
## iter 260 value 65.499664
## iter 270 value 59.669075
## iter 280 value 56.216714
## iter 290 value 54.570129
## iter 300 value 52.816969
## iter 310 value 50.279015
## iter 320 value 48.903057
## iter 330 value 47.876006
## iter 340 value 46.914086
## iter 350 value 45.868277
## iter 360 value 45.160162
## iter 370 value 44.577873
## iter 380 value 44.090507
## iter 390 value 43.633454
## iter 400 value 42.974325
## iter 410 value 42.539649
## iter 420 value 42.044556
## iter 430 value 41.526548
## iter 440 value 40.983889
## iter 450 value 40.330356
## iter 460 value 39.703912
## iter 470 value 39.173774
## iter 480 value 38.565787
## iter 490 value 38.224971
## iter 500 value 38.128785
## final  value 38.128785 
## stopped after 500 iterations
## # weights:  25
## initial  value 1393796.053730 
## iter  10 value 18857.730552
## iter  20 value 9089.766386
## iter  30 value 5218.038485
## iter  40 value 4301.270954
## iter  50 value 3431.792342
## iter  60 value 1713.123297
## iter  70 value 1172.351039
## iter  80 value 1097.952453
## iter  90 value 1091.164129
## iter 100 value 1085.692328
## iter 110 value 1068.756385
## iter 120 value 1062.486142
## iter 130 value 1060.163439
## iter 140 value 1059.731593
## iter 150 value 1059.685668
## iter 160 value 1058.705924
## iter 170 value 1056.881196
## iter 180 value 1052.158112
## iter 190 value 1045.128241
## iter 200 value 1040.531089
## iter 210 value 1027.221111
## iter 220 value 1027.168105
## iter 230 value 1025.658111
## iter 240 value 1022.985628
## iter 250 value 1021.218586
## iter 260 value 1020.936926
## final  value 1020.936237 
## converged
## # weights:  61
## initial  value 1415135.101805 
## iter  10 value 68135.086655
## iter  20 value 21209.036248
## iter  30 value 13634.985240
## iter  40 value 6768.509531
## iter  50 value 3695.247003
## iter  60 value 1831.902660
## iter  70 value 1320.895551
## iter  80 value 1118.088027
## iter  90 value 946.404895
## iter 100 value 874.382493
## iter 110 value 839.034551
## iter 120 value 819.364899
## iter 130 value 807.937227
## iter 140 value 804.877290
## iter 150 value 800.444953
## iter 160 value 784.667986
## iter 170 value 764.288047
## iter 180 value 753.578938
## iter 190 value 746.387702
## iter 200 value 739.714346
## iter 210 value 731.673786
## iter 220 value 726.625235
## iter 230 value 724.761479
## iter 240 value 723.679464
## iter 250 value 723.314921
## iter 260 value 723.285385
## iter 270 value 723.206132
## iter 280 value 723.147279
## iter 290 value 722.880587
## iter 300 value 721.488307
## iter 310 value 720.387845
## iter 320 value 718.863056
## iter 330 value 716.557346
## iter 340 value 715.053585
## iter 350 value 713.455378
## iter 360 value 712.764950
## iter 370 value 712.385598
## iter 380 value 712.357904
## iter 390 value 712.348027
## iter 400 value 712.315085
## iter 410 value 712.256688
## iter 420 value 711.981544
## iter 430 value 711.152699
## iter 440 value 710.165735
## iter 450 value 708.604658
## iter 460 value 707.418965
## iter 470 value 706.055333
## iter 480 value 704.572061
## iter 490 value 702.579047
## iter 500 value 702.118509
## final  value 702.118509 
## stopped after 500 iterations
## # weights:  121
## initial  value 1400987.024934 
## iter  10 value 2135.882190
## iter  20 value 948.926987
## iter  30 value 797.008111
## iter  40 value 689.331918
## iter  50 value 612.213711
## iter  60 value 582.466377
## iter  70 value 562.095010
## iter  80 value 544.370207
## iter  90 value 531.111514
## iter 100 value 518.684089
## iter 110 value 500.508092
## iter 120 value 484.879469
## iter 130 value 473.836759
## iter 140 value 465.796593
## iter 150 value 453.666833
## iter 160 value 444.400303
## iter 170 value 433.039128
## iter 180 value 426.691413
## iter 190 value 425.033926
## iter 200 value 422.940677
## iter 210 value 421.821123
## iter 220 value 420.477742
## iter 230 value 418.530599
## iter 240 value 416.470819
## iter 250 value 415.611406
## iter 260 value 415.498186
## iter 270 value 415.315141
## iter 280 value 414.949833
## iter 290 value 414.263174
## iter 300 value 413.038945
## iter 310 value 411.580138
## iter 320 value 409.121648
## iter 330 value 408.317525
## iter 340 value 407.762358
## iter 350 value 407.240295
## iter 360 value 406.829622
## iter 370 value 406.320458
## iter 380 value 405.916276
## iter 390 value 405.471216
## iter 400 value 405.036929
## iter 410 value 404.647924
## iter 420 value 404.290162
## iter 430 value 404.063425
## iter 440 value 403.846852
## iter 450 value 403.377675
## iter 460 value 403.333689
## iter 470 value 403.212174
## iter 480 value 402.473589
## iter 490 value 401.229760
## iter 500 value 399.788727
## final  value 399.788727 
## stopped after 500 iterations
## # weights:  181
## initial  value 1389588.623932 
## iter  10 value 1209.034656
## iter  20 value 800.501744
## iter  30 value 684.614919
## iter  40 value 548.261747
## iter  50 value 432.383495
## iter  60 value 370.828258
## iter  70 value 330.718987
## iter  80 value 276.394771
## iter  90 value 256.048623
## iter 100 value 239.096238
## iter 110 value 221.471541
## iter 120 value 205.469551
## iter 130 value 190.934949
## iter 140 value 181.089548
## iter 150 value 171.966430
## iter 160 value 167.114139
## iter 170 value 163.113775
## iter 180 value 158.923377
## iter 190 value 153.316265
## iter 200 value 148.112929
## iter 210 value 139.568078
## iter 220 value 136.039934
## iter 230 value 133.507435
## iter 240 value 131.004745
## iter 250 value 127.530518
## iter 260 value 124.556737
## iter 270 value 122.396522
## iter 280 value 117.948618
## iter 290 value 114.622309
## iter 300 value 111.884977
## iter 310 value 109.583676
## iter 320 value 107.179843
## iter 330 value 104.970756
## iter 340 value 102.789709
## iter 350 value 99.197505
## iter 360 value 95.556480
## iter 370 value 94.537676
## iter 380 value 94.119222
## iter 390 value 93.530980
## iter 400 value 92.677419
## iter 410 value 92.022061
## iter 420 value 90.625679
## iter 430 value 88.928104
## iter 440 value 87.474531
## iter 450 value 86.308564
## iter 460 value 85.511496
## iter 470 value 84.148714
## iter 480 value 82.820092
## iter 490 value 81.692135
## iter 500 value 81.063562
## final  value 81.063562 
## stopped after 500 iterations
## # weights:  241
## initial  value 1482965.235766 
## iter  10 value 1180.879252
## iter  20 value 814.111251
## iter  30 value 659.409591
## iter  40 value 579.276728
## iter  50 value 463.478803
## iter  60 value 339.511355
## iter  70 value 287.928845
## iter  80 value 247.352125
## iter  90 value 212.411769
## iter 100 value 178.425787
## iter 110 value 152.024930
## iter 120 value 139.148086
## iter 130 value 126.609525
## iter 140 value 114.505692
## iter 150 value 105.546412
## iter 160 value 99.983455
## iter 170 value 94.341533
## iter 180 value 89.267502
## iter 190 value 84.656162
## iter 200 value 81.053352
## iter 210 value 76.797241
## iter 220 value 73.249086
## iter 230 value 69.026442
## iter 240 value 64.938552
## iter 250 value 60.299851
## iter 260 value 54.017430
## iter 270 value 49.329686
## iter 280 value 46.182784
## iter 290 value 42.967297
## iter 300 value 39.851089
## iter 310 value 37.516642
## iter 320 value 35.625278
## iter 330 value 33.741665
## iter 340 value 32.275883
## iter 350 value 30.241303
## iter 360 value 29.237024
## iter 370 value 27.588382
## iter 380 value 26.333296
## iter 390 value 25.578765
## iter 400 value 24.829598
## iter 410 value 23.625415
## iter 420 value 22.672725
## iter 430 value 21.667207
## iter 440 value 21.032421
## iter 450 value 20.646403
## iter 460 value 20.188280
## iter 470 value 19.845840
## iter 480 value 19.555142
## iter 490 value 19.415572
## iter 500 value 19.351543
## final  value 19.351543 
## stopped after 500 iterations
## # weights:  25
## initial  value 1385756.953187 
## iter  10 value 48172.563992
## iter  20 value 5626.046095
## iter  30 value 4827.184454
## iter  40 value 4684.994733
## iter  50 value 4676.847180
## iter  60 value 4411.624885
## iter  70 value 4337.236652
## iter  80 value 4305.803371
## iter  90 value 4189.023750
## iter 100 value 4111.053620
## iter 110 value 4097.388796
## iter 120 value 4077.868661
## iter 130 value 4058.186724
## iter 140 value 4024.363131
## iter 150 value 4001.599711
## iter 160 value 3992.031933
## iter 170 value 3985.928854
## iter 180 value 3844.621387
## iter 190 value 3731.034289
## iter 200 value 3079.219451
## iter 210 value 2651.403268
## iter 220 value 1869.076982
## iter 230 value 1447.981206
## iter 240 value 1348.279574
## iter 250 value 1315.701274
## iter 260 value 1311.012222
## iter 270 value 1296.442451
## iter 280 value 1286.097193
## iter 290 value 1282.492933
## iter 300 value 1280.901038
## iter 310 value 1280.461069
## iter 320 value 1280.174577
## iter 330 value 1278.815732
## iter 340 value 1277.895013
## iter 350 value 1277.342544
## iter 360 value 1277.152740
## final  value 1277.152420 
## converged
## # weights:  61
## initial  value 1399122.577923 
## iter  10 value 8492.495248
## iter  20 value 3737.440331
## iter  30 value 3164.521992
## iter  40 value 2751.922036
## iter  50 value 2329.764530
## iter  60 value 1958.142304
## iter  70 value 1797.442093
## iter  80 value 1754.922723
## iter  90 value 1736.232238
## iter 100 value 1695.533168
## iter 110 value 1665.867370
## iter 120 value 1661.013312
## iter 130 value 1658.112296
## iter 140 value 1625.844994
## iter 150 value 1467.383800
## iter 160 value 1287.902896
## iter 170 value 1170.546110
## iter 180 value 1132.947532
## iter 190 value 1105.897044
## iter 200 value 1067.354388
## iter 210 value 1050.529468
## iter 220 value 1040.752608
## iter 230 value 1033.392759
## iter 240 value 1025.014724
## iter 250 value 1020.459613
## iter 260 value 1019.574775
## iter 270 value 1013.798544
## iter 280 value 1011.793458
## iter 290 value 1011.217186
## iter 300 value 1009.965131
## iter 310 value 1008.592208
## iter 320 value 1007.325138
## iter 330 value 1005.693383
## iter 340 value 1005.025584
## iter 350 value 1004.531955
## iter 360 value 1004.318232
## iter 370 value 1003.417727
## iter 380 value 1002.715534
## iter 390 value 1002.463412
## iter 400 value 1002.380161
## final  value 1002.380055 
## converged
## # weights:  121
## initial  value 1413298.565646 
## iter  10 value 1430.562028
## iter  20 value 831.031857
## iter  30 value 658.690126
## iter  40 value 549.857548
## iter  50 value 492.301158
## iter  60 value 457.480271
## iter  70 value 433.811995
## iter  80 value 416.642943
## iter  90 value 398.975130
## iter 100 value 381.526442
## iter 110 value 361.337671
## iter 120 value 340.486234
## iter 130 value 330.183381
## iter 140 value 317.941881
## iter 150 value 309.763997
## iter 160 value 301.814000
## iter 170 value 296.264440
## iter 180 value 292.844882
## iter 190 value 288.592533
## iter 200 value 284.673743
## iter 210 value 278.572477
## iter 220 value 273.092553
## iter 230 value 265.569153
## iter 240 value 259.581992
## iter 250 value 257.438313
## iter 260 value 256.415332
## iter 270 value 253.732371
## iter 280 value 250.500840
## iter 290 value 247.289329
## iter 300 value 244.434309
## iter 310 value 241.818178
## iter 320 value 239.240053
## iter 330 value 236.289890
## iter 340 value 230.583178
## iter 350 value 224.751983
## iter 360 value 222.301082
## iter 370 value 220.534332
## iter 380 value 219.531307
## iter 390 value 219.156432
## iter 400 value 218.913938
## iter 410 value 218.709599
## iter 420 value 218.642769
## iter 430 value 218.467114
## iter 440 value 218.375785
## iter 450 value 218.158281
## iter 460 value 218.078756
## iter 470 value 217.957158
## iter 480 value 217.891003
## iter 490 value 217.872053
## iter 500 value 217.871202
## final  value 217.871202 
## stopped after 500 iterations
## # weights:  181
## initial  value 1411394.011152 
## iter  10 value 1138.201808
## iter  20 value 771.813077
## iter  30 value 609.921321
## iter  40 value 473.322909
## iter  50 value 382.024387
## iter  60 value 338.760105
## iter  70 value 295.291145
## iter  80 value 251.255562
## iter  90 value 214.970369
## iter 100 value 199.301783
## iter 110 value 188.836892
## iter 120 value 177.175882
## iter 130 value 167.993402
## iter 140 value 157.269320
## iter 150 value 143.186740
## iter 160 value 129.694431
## iter 170 value 121.078541
## iter 180 value 111.482796
## iter 190 value 102.003307
## iter 200 value 96.673977
## iter 210 value 91.251334
## iter 220 value 85.943837
## iter 230 value 82.719689
## iter 240 value 79.065838
## iter 250 value 76.309919
## iter 260 value 74.108705
## iter 270 value 73.123644
## iter 280 value 72.172961
## iter 290 value 71.759515
## iter 300 value 71.367803
## iter 310 value 70.968709
## iter 320 value 70.501713
## iter 330 value 70.255363
## iter 340 value 70.122502
## iter 350 value 69.962639
## iter 360 value 69.874514
## iter 370 value 69.825143
## iter 380 value 69.794042
## iter 390 value 69.718004
## iter 400 value 69.618456
## iter 410 value 69.513349
## iter 420 value 69.303430
## iter 430 value 69.182564
## iter 440 value 69.093405
## iter 450 value 68.982104
## iter 460 value 68.868635
## iter 470 value 68.715874
## iter 480 value 68.271789
## iter 490 value 66.847164
## iter 500 value 65.995674
## final  value 65.995674 
## stopped after 500 iterations
## # weights:  241
## initial  value 1442424.607015 
## iter  10 value 1308.114413
## iter  20 value 739.043313
## iter  30 value 607.718410
## iter  40 value 519.468246
## iter  50 value 433.357689
## iter  60 value 362.754740
## iter  70 value 324.148434
## iter  80 value 295.712863
## iter  90 value 263.848253
## iter 100 value 240.841996
## iter 110 value 212.429790
## iter 120 value 185.662652
## iter 130 value 164.931948
## iter 140 value 141.721610
## iter 150 value 126.710512
## iter 160 value 117.472936
## iter 170 value 108.069620
## iter 180 value 101.634520
## iter 190 value 90.423121
## iter 200 value 81.015868
## iter 210 value 73.964015
## iter 220 value 67.833524
## iter 230 value 64.017169
## iter 240 value 60.042701
## iter 250 value 56.459999
## iter 260 value 54.295065
## iter 270 value 52.049504
## iter 280 value 49.624346
## iter 290 value 48.404838
## iter 300 value 46.712110
## iter 310 value 44.551690
## iter 320 value 43.024047
## iter 330 value 41.128716
## iter 340 value 39.114657
## iter 350 value 36.356922
## iter 360 value 34.593786
## iter 370 value 33.541986
## iter 380 value 32.857202
## iter 390 value 32.321888
## iter 400 value 31.713845
## iter 410 value 31.328039
## iter 420 value 30.922180
## iter 430 value 30.410621
## iter 440 value 29.946491
## iter 450 value 29.559267
## iter 460 value 29.084275
## iter 470 value 28.642889
## iter 480 value 28.374166
## iter 490 value 28.220594
## iter 500 value 28.154890
## final  value 28.154890 
## stopped after 500 iterations
## # weights:  25
## initial  value 1410405.784373 
## iter  10 value 17593.289730
## iter  20 value 15479.109890
## iter  30 value 14370.467707
## iter  40 value 13529.388845
## iter  50 value 8311.475210
## iter  60 value 3074.308761
## iter  70 value 1828.148546
## iter  80 value 1447.956967
## iter  90 value 1387.715565
## iter 100 value 1338.917764
## iter 110 value 1283.525839
## iter 120 value 1279.470971
## iter 130 value 1279.434373
## iter 130 value 1279.434362
## final  value 1279.434362 
## converged
## # weights:  61
## initial  value 1390495.319656 
## iter  10 value 4262.310965
## iter  20 value 3135.835201
## iter  30 value 2607.914753
## iter  40 value 2294.598266
## iter  50 value 1947.171829
## iter  60 value 1749.449614
## iter  70 value 1682.371193
## iter  80 value 1570.643663
## iter  90 value 1369.438522
## iter 100 value 1238.022184
## iter 110 value 1114.010623
## iter 120 value 1048.198666
## iter 130 value 1024.750346
## iter 140 value 1007.624594
## iter 150 value 965.220398
## iter 160 value 904.860688
## iter 170 value 876.086587
## iter 180 value 851.829014
## iter 190 value 819.365860
## iter 200 value 786.670386
## iter 210 value 774.455769
## iter 220 value 764.041146
## iter 230 value 755.453109
## iter 240 value 751.936619
## iter 250 value 749.143105
## iter 260 value 748.600057
## iter 270 value 747.053135
## iter 280 value 742.237694
## iter 290 value 736.071864
## iter 300 value 732.123105
## iter 310 value 731.152801
## iter 320 value 730.546548
## iter 330 value 730.496267
## iter 340 value 730.494612
## final  value 730.494297 
## converged
## # weights:  121
## initial  value 1428212.309370 
## iter  10 value 3686.633408
## iter  20 value 1838.579675
## iter  30 value 1330.776320
## iter  40 value 1189.277562
## iter  50 value 1096.744237
## iter  60 value 1055.879668
## iter  70 value 1014.891592
## iter  80 value 976.702369
## iter  90 value 941.322410
## iter 100 value 866.271912
## iter 110 value 817.432624
## iter 120 value 784.615994
## iter 130 value 754.499144
## iter 140 value 728.037059
## iter 150 value 711.028241
## iter 160 value 696.247781
## iter 170 value 681.102139
## iter 180 value 668.257284
## iter 190 value 660.414762
## iter 200 value 641.482853
## iter 210 value 626.642362
## iter 220 value 613.256818
## iter 230 value 606.607440
## iter 240 value 597.809778
## iter 250 value 595.791958
## iter 260 value 594.046556
## iter 270 value 592.251462
## iter 280 value 590.834968
## iter 290 value 589.602787
## iter 300 value 588.554477
## iter 310 value 585.820644
## iter 320 value 582.637223
## iter 330 value 580.338698
## iter 340 value 578.697859
## iter 350 value 575.668634
## iter 360 value 571.347476
## iter 370 value 568.293413
## iter 380 value 564.898680
## iter 390 value 564.071284
## iter 400 value 563.636708
## iter 410 value 563.228502
## iter 420 value 563.144202
## iter 430 value 563.128731
## iter 440 value 563.125472
## iter 450 value 563.124620
## final  value 563.124485 
## converged
## # weights:  181
## initial  value 1345794.900611 
## iter  10 value 1269.060245
## iter  20 value 861.238077
## iter  30 value 708.115266
## iter  40 value 632.558192
## iter  50 value 566.335817
## iter  60 value 530.295342
## iter  70 value 494.268841
## iter  80 value 475.722849
## iter  90 value 461.686500
## iter 100 value 449.808581
## iter 110 value 438.999394
## iter 120 value 432.328629
## iter 130 value 427.636549
## iter 140 value 424.115737
## iter 150 value 419.410500
## iter 160 value 415.638753
## iter 170 value 412.910816
## iter 180 value 410.519073
## iter 190 value 407.769599
## iter 200 value 404.804591
## iter 210 value 401.424497
## iter 220 value 400.311839
## iter 230 value 398.983355
## iter 240 value 397.742900
## iter 250 value 396.350654
## iter 260 value 395.551638
## iter 270 value 394.917512
## iter 280 value 393.951534
## iter 290 value 393.186533
## iter 300 value 392.509012
## iter 310 value 392.024675
## iter 320 value 391.793276
## iter 330 value 391.497790
## iter 340 value 390.992714
## iter 350 value 389.928879
## iter 360 value 388.656630
## iter 370 value 387.838451
## iter 380 value 387.167161
## iter 390 value 386.024954
## iter 400 value 384.929248
## iter 410 value 384.282733
## iter 420 value 383.735951
## iter 430 value 383.369506
## iter 440 value 383.217827
## iter 450 value 383.149137
## iter 460 value 383.063970
## iter 470 value 382.964769
## iter 480 value 382.745338
## iter 490 value 381.873027
## iter 500 value 380.320393
## final  value 380.320393 
## stopped after 500 iterations
## # weights:  241
## initial  value 1442893.265919 
## iter  10 value 1865.163565
## iter  20 value 953.755893
## iter  30 value 709.643692
## iter  40 value 602.663354
## iter  50 value 546.960928
## iter  60 value 511.975300
## iter  70 value 488.632974
## iter  80 value 476.636292
## iter  90 value 467.428162
## iter 100 value 460.469860
## iter 110 value 455.010734
## iter 120 value 450.916494
## iter 130 value 444.209310
## iter 140 value 437.537908
## iter 150 value 431.553150
## iter 160 value 425.673790
## iter 170 value 418.708580
## iter 180 value 412.090185
## iter 190 value 403.777857
## iter 200 value 398.676912
## iter 210 value 393.541194
## iter 220 value 389.667371
## iter 230 value 385.144506
## iter 240 value 381.277392
## iter 250 value 376.866504
## iter 260 value 372.120153
## iter 270 value 366.715133
## iter 280 value 360.492842
## iter 290 value 355.775235
## iter 300 value 352.415857
## iter 310 value 349.824220
## iter 320 value 346.198988
## iter 330 value 343.231419
## iter 340 value 340.795564
## iter 350 value 338.678983
## iter 360 value 336.201743
## iter 370 value 334.083586
## iter 380 value 331.356664
## iter 390 value 328.056124
## iter 400 value 325.803056
## iter 410 value 324.324926
## iter 420 value 322.705850
## iter 430 value 321.239330
## iter 440 value 320.085876
## iter 450 value 319.363352
## iter 460 value 318.809280
## iter 470 value 318.312681
## iter 480 value 317.728673
## iter 490 value 317.400780
## iter 500 value 316.925133
## final  value 316.925133 
## stopped after 500 iterations
## # weights:  25
## initial  value 1376505.554393 
## iter  10 value 13868.910719
## iter  20 value 8980.535514
## iter  30 value 6038.288622
## iter  40 value 5217.029749
## iter  50 value 5179.685831
## iter  60 value 5080.343651
## iter  70 value 2615.825485
## iter  80 value 1623.733020
## iter  90 value 1383.242726
## iter 100 value 1321.905558
## iter 110 value 1307.565600
## iter 120 value 1305.432616
## iter 130 value 1299.419336
## iter 140 value 1284.128492
## iter 150 value 1249.348190
## iter 160 value 1124.950146
## iter 170 value 1099.944447
## iter 180 value 1090.267213
## iter 190 value 1090.168701
## iter 200 value 1090.153358
## iter 210 value 1089.992062
## final  value 1089.809081 
## converged
## # weights:  61
## initial  value 1408902.183080 
## iter  10 value 12056.134414
## iter  20 value 6786.434834
## iter  30 value 4103.598224
## iter  40 value 2740.811109
## iter  50 value 2206.431614
## iter  60 value 1985.524291
## iter  70 value 1895.275355
## iter  80 value 1873.187740
## iter  90 value 1854.557052
## iter 100 value 1689.612057
## iter 110 value 1637.186818
## iter 120 value 1582.134762
## iter 130 value 1572.210134
## iter 140 value 1568.897402
## iter 150 value 1542.925975
## iter 160 value 1502.448869
## iter 170 value 1445.021854
## iter 180 value 1399.038459
## iter 190 value 1374.345424
## iter 200 value 1363.518485
## iter 210 value 1338.337040
## iter 220 value 1290.280435
## iter 230 value 1216.734581
## iter 240 value 1153.426899
## iter 250 value 1007.168582
## iter 260 value 892.692768
## iter 270 value 866.526303
## iter 280 value 859.414305
## iter 290 value 851.906647
## iter 300 value 849.736325
## iter 310 value 846.871904
## iter 320 value 837.461969
## iter 330 value 817.559288
## iter 340 value 788.826860
## iter 350 value 769.561769
## iter 360 value 758.767509
## iter 370 value 752.466283
## iter 380 value 739.150897
## iter 390 value 732.598750
## iter 400 value 723.794736
## iter 410 value 717.847091
## iter 420 value 705.051669
## iter 430 value 696.083845
## iter 440 value 685.644900
## iter 450 value 680.108713
## iter 460 value 676.972635
## iter 470 value 674.410857
## iter 480 value 673.415600
## iter 490 value 660.386460
## iter 500 value 645.396803
## final  value 645.396803 
## stopped after 500 iterations
## # weights:  121
## initial  value 1411255.962266 
## iter  10 value 1490.381268
## iter  20 value 905.060486
## iter  30 value 683.587451
## iter  40 value 598.317435
## iter  50 value 534.422245
## iter  60 value 441.273190
## iter  70 value 414.410749
## iter  80 value 390.902814
## iter  90 value 362.980172
## iter 100 value 342.012342
## iter 110 value 326.031875
## iter 120 value 312.783921
## iter 130 value 300.929641
## iter 140 value 291.513548
## iter 150 value 284.126638
## iter 160 value 280.585951
## iter 170 value 276.933450
## iter 180 value 274.348873
## iter 190 value 272.347741
## iter 200 value 269.519700
## iter 210 value 264.517179
## iter 220 value 259.249750
## iter 230 value 255.828173
## iter 240 value 253.653170
## iter 250 value 252.758477
## iter 260 value 252.453785
## iter 270 value 251.602907
## iter 280 value 250.131769
## iter 290 value 248.704165
## iter 300 value 246.564365
## iter 310 value 245.304647
## iter 320 value 244.549361
## iter 330 value 243.003802
## iter 340 value 241.492672
## iter 350 value 240.785929
## iter 360 value 240.328112
## iter 370 value 239.977742
## iter 380 value 239.681435
## iter 390 value 239.559955
## iter 400 value 239.442395
## iter 410 value 239.274984
## iter 420 value 239.211569
## iter 430 value 239.118821
## iter 440 value 238.978421
## iter 450 value 238.840068
## iter 460 value 238.780848
## iter 470 value 238.753874
## iter 480 value 238.743771
## iter 490 value 238.738167
## iter 500 value 238.736915
## final  value 238.736915 
## stopped after 500 iterations
## # weights:  181
## initial  value 1434844.665530 
## iter  10 value 1185.311557
## iter  20 value 729.008955
## iter  30 value 585.632432
## iter  40 value 492.309969
## iter  50 value 380.691003
## iter  60 value 317.470154
## iter  70 value 291.389197
## iter  80 value 264.042453
## iter  90 value 238.102406
## iter 100 value 219.855123
## iter 110 value 204.286285
## iter 120 value 193.042675
## iter 130 value 185.969703
## iter 140 value 176.668448
## iter 150 value 166.170960
## iter 160 value 157.944158
## iter 170 value 152.446573
## iter 180 value 147.904029
## iter 190 value 142.566184
## iter 200 value 137.594204
## iter 210 value 134.135272
## iter 220 value 131.982816
## iter 230 value 130.113828
## iter 240 value 125.477843
## iter 250 value 122.079830
## iter 260 value 120.025121
## iter 270 value 118.649260
## iter 280 value 117.421662
## iter 290 value 116.457269
## iter 300 value 115.672659
## iter 310 value 114.885789
## iter 320 value 113.865869
## iter 330 value 113.224084
## iter 340 value 112.765528
## iter 350 value 112.226092
## iter 360 value 111.505582
## iter 370 value 111.245006
## iter 380 value 111.037344
## iter 390 value 110.713179
## iter 400 value 110.219483
## iter 410 value 109.833558
## iter 420 value 109.125251
## iter 430 value 108.294574
## iter 440 value 107.338099
## iter 450 value 106.324685
## iter 460 value 105.617135
## iter 470 value 104.993454
## iter 480 value 104.626200
## iter 490 value 104.389374
## iter 500 value 104.196012
## final  value 104.196012 
## stopped after 500 iterations
## # weights:  241
## initial  value 1382697.281020 
## iter  10 value 1494.648186
## iter  20 value 712.308506
## iter  30 value 527.943661
## iter  40 value 409.807358
## iter  50 value 332.540763
## iter  60 value 274.215678
## iter  70 value 219.888255
## iter  80 value 193.585897
## iter  90 value 171.242988
## iter 100 value 155.527566
## iter 110 value 143.697548
## iter 120 value 134.031114
## iter 130 value 124.451259
## iter 140 value 113.994376
## iter 150 value 106.927591
## iter 160 value 102.613632
## iter 170 value 99.022618
## iter 180 value 93.738847
## iter 190 value 89.252408
## iter 200 value 85.705674
## iter 210 value 82.950109
## iter 220 value 79.198687
## iter 230 value 75.781224
## iter 240 value 72.628231
## iter 250 value 70.803989
## iter 260 value 69.114068
## iter 270 value 67.993148
## iter 280 value 67.196932
## iter 290 value 66.207836
## iter 300 value 65.201641
## iter 310 value 64.221914
## iter 320 value 63.486784
## iter 330 value 62.564274
## iter 340 value 61.554905
## iter 350 value 60.544679
## iter 360 value 59.622188
## iter 370 value 59.016570
## iter 380 value 58.374077
## iter 390 value 57.713778
## iter 400 value 57.231774
## iter 410 value 56.816231
## iter 420 value 56.448915
## iter 430 value 56.160388
## iter 440 value 55.930147
## iter 450 value 55.700594
## iter 460 value 55.378283
## iter 470 value 55.087046
## iter 480 value 54.762523
## iter 490 value 54.600801
## iter 500 value 54.550072
## final  value 54.550072 
## stopped after 500 iterations
## # weights:  25
## initial  value 1409918.064319 
## iter  10 value 16893.263944
## iter  20 value 15302.774225
## iter  30 value 12099.583290
## iter  40 value 9192.735113
## iter  50 value 8888.488887
## iter  60 value 8839.652696
## iter  70 value 6718.960099
## iter  80 value 5337.688832
## iter  90 value 4834.711055
## iter 100 value 3352.467220
## iter 110 value 2894.156822
## iter 120 value 2316.224239
## iter 130 value 2261.674947
## iter 140 value 2261.422176
## iter 150 value 2259.329692
## iter 160 value 1357.506671
## iter 170 value 1180.449218
## iter 180 value 974.026953
## iter 190 value 874.314257
## iter 200 value 859.967624
## iter 210 value 857.026931
## iter 220 value 855.064715
## iter 230 value 853.108099
## iter 240 value 845.368977
## iter 250 value 837.708381
## iter 260 value 833.643959
## iter 270 value 833.214984
## iter 280 value 831.791996
## iter 290 value 830.341479
## iter 300 value 830.215437
## iter 310 value 830.196424
## final  value 830.196334 
## converged
## # weights:  61
## initial  value 1408705.009561 
## iter  10 value 42011.707047
## iter  20 value 18061.828053
## iter  30 value 12755.860611
## iter  40 value 7874.913856
## iter  50 value 5827.241719
## iter  60 value 2231.529316
## iter  70 value 1312.683623
## iter  80 value 1156.240746
## iter  90 value 1115.120369
## iter 100 value 1096.877136
## iter 110 value 1091.418821
## iter 120 value 1083.951402
## iter 130 value 1083.099785
## iter 140 value 1051.439555
## iter 150 value 958.466483
## iter 160 value 923.329956
## iter 170 value 910.887116
## iter 180 value 898.805779
## iter 190 value 892.868977
## iter 200 value 892.679045
## iter 210 value 891.108921
## iter 220 value 889.135654
## iter 230 value 871.444180
## iter 240 value 870.641115
## iter 250 value 847.287042
## iter 260 value 782.500123
## iter 270 value 772.867155
## iter 280 value 772.136430
## iter 290 value 771.106383
## iter 300 value 738.407268
## iter 310 value 715.016983
## iter 320 value 700.098522
## iter 330 value 688.514464
## iter 340 value 676.040525
## iter 350 value 673.901242
## iter 360 value 673.815076
## iter 370 value 672.634777
## iter 380 value 671.376641
## iter 390 value 670.264855
## iter 400 value 668.887482
## iter 410 value 667.570217
## iter 420 value 665.456823
## iter 430 value 661.762575
## iter 440 value 661.576117
## iter 450 value 661.077477
## iter 460 value 660.645482
## iter 470 value 660.124962
## iter 480 value 660.115285
## iter 490 value 659.814365
## iter 500 value 658.537665
## final  value 658.537665 
## stopped after 500 iterations
## # weights:  121
## initial  value 1472655.813606 
## iter  10 value 1955.678658
## iter  20 value 967.138206
## iter  30 value 740.212796
## iter  40 value 608.146968
## iter  50 value 540.375532
## iter  60 value 498.843832
## iter  70 value 453.342288
## iter  80 value 395.636875
## iter  90 value 369.976050
## iter 100 value 342.689526
## iter 110 value 325.108109
## iter 120 value 306.182809
## iter 130 value 300.342116
## iter 140 value 294.273172
## iter 150 value 284.756573
## iter 160 value 279.982611
## iter 170 value 275.678463
## iter 180 value 272.371894
## iter 190 value 269.176347
## iter 200 value 265.398931
## iter 210 value 263.277047
## iter 220 value 261.840700
## iter 230 value 259.410334
## iter 240 value 257.320412
## iter 250 value 256.164856
## iter 260 value 255.704230
## iter 270 value 254.796339
## iter 280 value 253.489994
## iter 290 value 252.506774
## iter 300 value 251.063705
## iter 310 value 249.139549
## iter 320 value 245.508092
## iter 330 value 241.963948
## iter 340 value 239.468051
## iter 350 value 238.006131
## iter 360 value 237.436581
## iter 370 value 237.208327
## iter 380 value 236.880516
## iter 390 value 236.495119
## iter 400 value 235.407576
## iter 410 value 233.645776
## iter 420 value 231.394869
## iter 430 value 228.652856
## iter 440 value 227.900540
## iter 450 value 227.482930
## iter 460 value 226.739046
## iter 470 value 225.402077
## iter 480 value 223.340278
## iter 490 value 222.201775
## iter 500 value 222.107644
## final  value 222.107644 
## stopped after 500 iterations
## # weights:  181
## initial  value 1383724.921133 
## iter  10 value 1140.656765
## iter  20 value 686.973616
## iter  30 value 559.317301
## iter  40 value 468.093637
## iter  50 value 389.913350
## iter  60 value 343.378496
## iter  70 value 318.371112
## iter  80 value 294.147930
## iter  90 value 263.745853
## iter 100 value 241.179235
## iter 110 value 228.120881
## iter 120 value 213.568472
## iter 130 value 200.768174
## iter 140 value 185.377903
## iter 150 value 174.573550
## iter 160 value 163.500204
## iter 170 value 151.740527
## iter 180 value 139.544220
## iter 190 value 132.752271
## iter 200 value 125.941150
## iter 210 value 120.002828
## iter 220 value 114.665134
## iter 230 value 109.860045
## iter 240 value 107.289971
## iter 250 value 104.186802
## iter 260 value 99.617444
## iter 270 value 96.797307
## iter 280 value 94.459654
## iter 290 value 93.375418
## iter 300 value 92.080426
## iter 310 value 90.662937
## iter 320 value 89.807696
## iter 330 value 88.390869
## iter 340 value 86.613391
## iter 350 value 85.159823
## iter 360 value 84.438102
## iter 370 value 84.154286
## iter 380 value 83.962719
## iter 390 value 83.727025
## iter 400 value 83.339513
## iter 410 value 83.007732
## iter 420 value 82.647262
## iter 430 value 82.240526
## iter 440 value 81.336969
## iter 450 value 80.335584
## iter 460 value 79.727292
## iter 470 value 79.484365
## iter 480 value 79.144365
## iter 490 value 78.480179
## iter 500 value 78.094737
## final  value 78.094737 
## stopped after 500 iterations
## # weights:  241
## initial  value 1394784.426832 
## iter  10 value 1253.851802
## iter  20 value 742.479039
## iter  30 value 578.272411
## iter  40 value 489.540601
## iter  50 value 398.356679
## iter  60 value 338.733747
## iter  70 value 303.363370
## iter  80 value 276.360479
## iter  90 value 243.114193
## iter 100 value 220.207056
## iter 110 value 197.899952
## iter 120 value 185.889807
## iter 130 value 173.453186
## iter 140 value 160.775360
## iter 150 value 151.121251
## iter 160 value 142.816426
## iter 170 value 136.781967
## iter 180 value 130.793148
## iter 190 value 124.314552
## iter 200 value 117.383339
## iter 210 value 110.886471
## iter 220 value 98.361320
## iter 230 value 85.323995
## iter 240 value 73.933832
## iter 250 value 66.368620
## iter 260 value 60.642077
## iter 270 value 57.336438
## iter 280 value 55.126354
## iter 290 value 53.380265
## iter 300 value 51.583153
## iter 310 value 49.639812
## iter 320 value 47.983363
## iter 330 value 46.563244
## iter 340 value 45.087715
## iter 350 value 43.629601
## iter 360 value 42.683533
## iter 370 value 42.081269
## iter 380 value 41.567769
## iter 390 value 40.999653
## iter 400 value 40.584576
## iter 410 value 40.123338
## iter 420 value 39.749706
## iter 430 value 39.406155
## iter 440 value 38.870857
## iter 450 value 38.439063
## iter 460 value 38.013985
## iter 470 value 37.611351
## iter 480 value 37.257887
## iter 490 value 37.106855
## iter 500 value 37.072628
## final  value 37.072628 
## stopped after 500 iterations
## # weights:  25
## initial  value 1407644.724686 
## iter  10 value 16277.244852
## iter  20 value 15396.841019
## iter  30 value 14813.892143
## iter  40 value 9830.005938
## iter  50 value 7195.441831
## iter  60 value 6019.399218
## iter  70 value 1952.695987
## iter  80 value 1328.307151
## iter  90 value 1309.660242
## iter 100 value 1296.229323
## iter 110 value 1285.421011
## iter 120 value 1279.037974
## iter 130 value 1242.900639
## iter 140 value 1233.141077
## iter 150 value 1185.378860
## iter 160 value 1158.208431
## iter 170 value 1149.625412
## iter 180 value 1148.725795
## iter 190 value 1135.448805
## iter 200 value 1120.301644
## iter 210 value 1119.240143
## iter 220 value 1118.506630
## iter 230 value 1118.422777
## iter 240 value 1118.411343
## iter 250 value 1117.964757
## iter 260 value 1117.745636
## iter 270 value 1117.344654
## iter 280 value 1117.277587
## iter 290 value 1117.272064
## iter 300 value 1117.196742
## iter 310 value 1117.111445
## iter 320 value 1117.044745
## iter 330 value 1117.017393
## iter 330 value 1117.017386
## iter 330 value 1117.017377
## final  value 1117.017377 
## converged
## # weights:  61
## initial  value 1397988.989015 
## iter  10 value 3669.274819
## iter  20 value 2112.660533
## iter  30 value 1640.147688
## iter  40 value 1191.984673
## iter  50 value 961.632449
## iter  60 value 859.877114
## iter  70 value 809.550392
## iter  80 value 783.265856
## iter  90 value 771.364396
## iter 100 value 763.211778
## iter 110 value 749.820589
## iter 120 value 741.040302
## iter 130 value 710.571399
## iter 140 value 667.403487
## iter 150 value 626.883736
## iter 160 value 593.385051
## iter 170 value 582.309648
## iter 180 value 576.042018
## iter 190 value 569.169888
## iter 200 value 561.762029
## iter 210 value 556.103482
## iter 220 value 548.415678
## iter 230 value 543.044846
## iter 240 value 542.498018
## iter 250 value 541.369651
## iter 260 value 539.949210
## iter 270 value 536.955769
## iter 280 value 532.886975
## iter 290 value 530.887362
## iter 300 value 529.638641
## iter 310 value 529.511863
## iter 320 value 529.366997
## iter 330 value 529.158114
## iter 340 value 528.916571
## iter 350 value 528.762322
## iter 360 value 528.760069
## iter 370 value 528.758004
## iter 380 value 528.744570
## iter 390 value 528.676824
## iter 400 value 528.612937
## iter 410 value 528.499717
## iter 420 value 528.447319
## iter 430 value 528.411325
## iter 440 value 528.401391
## iter 450 value 528.377269
## iter 460 value 528.354332
## iter 470 value 528.264071
## iter 480 value 528.252433
## iter 490 value 528.251977
## iter 500 value 528.250943
## final  value 528.250943 
## stopped after 500 iterations
## # weights:  121
## initial  value 1358735.038285 
## iter  10 value 2463.261607
## iter  20 value 1216.252666
## iter  30 value 897.187740
## iter  40 value 696.325529
## iter  50 value 582.149018
## iter  60 value 538.988896
## iter  70 value 487.461115
## iter  80 value 460.958088
## iter  90 value 445.734343
## iter 100 value 431.387491
## iter 110 value 415.875541
## iter 120 value 409.663683
## iter 130 value 403.195717
## iter 140 value 401.443582
## iter 150 value 400.336842
## iter 160 value 397.436731
## iter 170 value 395.110769
## iter 180 value 392.062143
## iter 190 value 388.808816
## iter 200 value 383.379302
## iter 210 value 379.618195
## iter 220 value 378.922398
## iter 230 value 377.922424
## iter 240 value 376.526349
## iter 250 value 376.308139
## iter 260 value 375.628835
## iter 270 value 372.969445
## iter 280 value 369.554490
## iter 290 value 368.738217
## iter 300 value 368.162666
## iter 310 value 367.681318
## iter 320 value 366.030203
## iter 330 value 361.445395
## iter 340 value 356.674878
## iter 350 value 354.475398
## iter 360 value 353.684000
## iter 370 value 353.374002
## iter 380 value 352.659889
## iter 390 value 351.960926
## iter 400 value 351.672607
## iter 410 value 350.725966
## iter 420 value 349.412795
## iter 430 value 348.562503
## iter 440 value 348.428758
## iter 450 value 348.273730
## iter 460 value 347.574359
## iter 470 value 347.218471
## iter 480 value 346.965740
## iter 490 value 346.704465
## iter 500 value 346.213570
## final  value 346.213570 
## stopped after 500 iterations
## # weights:  181
## initial  value 1370357.622741 
## iter  10 value 1121.493856
## iter  20 value 695.219403
## iter  30 value 581.608939
## iter  40 value 492.097311
## iter  50 value 448.804968
## iter  60 value 404.885226
## iter  70 value 358.554775
## iter  80 value 321.232545
## iter  90 value 295.875056
## iter 100 value 278.257830
## iter 110 value 261.910019
## iter 120 value 247.400431
## iter 130 value 234.017067
## iter 140 value 219.051249
## iter 150 value 206.700902
## iter 160 value 187.422800
## iter 170 value 173.449285
## iter 180 value 161.978143
## iter 190 value 153.532301
## iter 200 value 146.718448
## iter 210 value 139.882402
## iter 220 value 134.211850
## iter 230 value 126.972606
## iter 240 value 119.717907
## iter 250 value 114.537267
## iter 260 value 109.870417
## iter 270 value 106.887438
## iter 280 value 104.701010
## iter 290 value 102.793551
## iter 300 value 99.719194
## iter 310 value 94.919944
## iter 320 value 92.910435
## iter 330 value 91.598985
## iter 340 value 90.516666
## iter 350 value 89.884020
## iter 360 value 88.944959
## iter 370 value 88.391358
## iter 380 value 88.146366
## iter 390 value 87.753996
## iter 400 value 87.239165
## iter 410 value 86.656953
## iter 420 value 86.154361
## iter 430 value 85.718198
## iter 440 value 85.251172
## iter 450 value 84.123822
## iter 460 value 83.179985
## iter 470 value 82.241737
## iter 480 value 81.410076
## iter 490 value 80.148957
## iter 500 value 78.791758
## final  value 78.791758 
## stopped after 500 iterations
## # weights:  241
## initial  value 1394531.629793 
## iter  10 value 1556.418468
## iter  20 value 728.020973
## iter  30 value 569.930840
## iter  40 value 455.452673
## iter  50 value 361.510200
## iter  60 value 292.698292
## iter  70 value 248.520215
## iter  80 value 202.666181
## iter  90 value 168.119152
## iter 100 value 149.200400
## iter 110 value 127.457669
## iter 120 value 115.276168
## iter 130 value 104.395697
## iter 140 value 96.243217
## iter 150 value 92.102976
## iter 160 value 87.184904
## iter 170 value 83.242311
## iter 180 value 79.704211
## iter 190 value 75.545963
## iter 200 value 70.224061
## iter 210 value 64.986427
## iter 220 value 61.292671
## iter 230 value 57.970208
## iter 240 value 54.032832
## iter 250 value 50.480777
## iter 260 value 48.385493
## iter 270 value 46.438990
## iter 280 value 44.144883
## iter 290 value 41.786265
## iter 300 value 40.026170
## iter 310 value 38.293568
## iter 320 value 37.293692
## iter 330 value 36.415948
## iter 340 value 35.432999
## iter 350 value 34.420496
## iter 360 value 33.672526
## iter 370 value 32.994990
## iter 380 value 32.179746
## iter 390 value 31.583076
## iter 400 value 31.181869
## iter 410 value 30.753296
## iter 420 value 30.464756
## iter 430 value 30.136672
## iter 440 value 29.806567
## iter 450 value 29.378190
## iter 460 value 28.984268
## iter 470 value 28.610054
## iter 480 value 28.406269
## iter 490 value 28.301100
## iter 500 value 28.274641
## final  value 28.274641 
## stopped after 500 iterations
## # weights:  25
## initial  value 1393612.292566 
## iter  10 value 6291.180642
## iter  20 value 6265.542738
## iter  20 value 6265.542688
## iter  20 value 6265.542688
## final  value 6265.542688 
## converged
## # weights:  61
## initial  value 1440695.729584 
## iter  10 value 62290.459830
## iter  20 value 4751.281675
## iter  30 value 3775.951060
## iter  40 value 3030.171394
## iter  50 value 1510.024531
## iter  60 value 1357.852890
## iter  70 value 1259.185632
## iter  80 value 1073.131229
## iter  90 value 988.031675
## iter 100 value 953.456285
## iter 110 value 934.835086
## iter 120 value 917.985774
## iter 130 value 894.530240
## iter 140 value 873.971712
## iter 150 value 839.706330
## iter 160 value 789.367274
## iter 170 value 765.278573
## iter 180 value 755.006050
## iter 190 value 750.621252
## iter 200 value 745.743410
## iter 210 value 736.405121
## iter 220 value 724.966776
## iter 230 value 716.342378
## iter 240 value 713.736118
## iter 250 value 711.474880
## iter 260 value 708.880750
## iter 270 value 704.791343
## iter 280 value 700.433575
## iter 290 value 697.728291
## iter 300 value 695.677807
## iter 310 value 695.228944
## iter 320 value 694.280494
## iter 330 value 693.561119
## iter 340 value 691.330622
## iter 350 value 688.668165
## iter 360 value 686.852851
## iter 370 value 683.552386
## iter 380 value 682.499758
## iter 390 value 678.883741
## iter 400 value 666.755233
## iter 410 value 642.387422
## iter 420 value 634.509208
## iter 430 value 634.455179
## iter 440 value 633.785330
## iter 450 value 632.290941
## iter 460 value 629.927131
## iter 470 value 627.166726
## iter 480 value 627.104873
## iter 490 value 626.487275
## iter 500 value 622.075383
## final  value 622.075383 
## stopped after 500 iterations
## # weights:  121
## initial  value 1390036.315580 
## iter  10 value 2272.500271
## iter  20 value 938.577558
## iter  30 value 764.399625
## iter  40 value 642.698333
## iter  50 value 581.513296
## iter  60 value 525.736839
## iter  70 value 492.709877
## iter  80 value 470.359903
## iter  90 value 449.420348
## iter 100 value 424.244315
## iter 110 value 401.822055
## iter 120 value 381.573619
## iter 130 value 359.346680
## iter 140 value 351.729574
## iter 150 value 339.274110
## iter 160 value 332.838280
## iter 170 value 324.992645
## iter 180 value 317.912289
## iter 190 value 314.029811
## iter 200 value 310.151792
## iter 210 value 307.097278
## iter 220 value 304.928401
## iter 230 value 302.199881
## iter 240 value 299.556713
## iter 250 value 297.855113
## iter 260 value 297.230559
## iter 270 value 296.378942
## iter 280 value 295.045725
## iter 290 value 293.436863
## iter 300 value 291.741293
## iter 310 value 290.553597
## iter 320 value 289.832470
## iter 330 value 289.363368
## iter 340 value 288.760788
## iter 350 value 286.622029
## iter 360 value 284.805223
## iter 370 value 283.438312
## iter 380 value 282.130052
## iter 390 value 281.683860
## iter 400 value 281.353352
## iter 410 value 281.322331
## iter 420 value 281.279402
## iter 430 value 281.172870
## iter 440 value 280.959851
## iter 450 value 280.740546
## iter 460 value 280.643382
## iter 470 value 280.553348
## iter 480 value 280.439934
## iter 490 value 280.339714
## iter 500 value 280.281032
## final  value 280.281032 
## stopped after 500 iterations
## # weights:  181
## initial  value 1406500.058150 
## iter  10 value 1205.902608
## iter  20 value 808.220355
## iter  30 value 650.812361
## iter  40 value 529.300351
## iter  50 value 441.835262
## iter  60 value 382.485628
## iter  70 value 316.382992
## iter  80 value 280.632855
## iter  90 value 260.914881
## iter 100 value 239.610672
## iter 110 value 223.945286
## iter 120 value 202.637381
## iter 130 value 186.416540
## iter 140 value 174.871875
## iter 150 value 165.592737
## iter 160 value 159.621926
## iter 170 value 151.937088
## iter 180 value 145.573525
## iter 190 value 140.444637
## iter 200 value 133.862973
## iter 210 value 128.340377
## iter 220 value 124.292003
## iter 230 value 121.244917
## iter 240 value 119.460431
## iter 250 value 117.775155
## iter 260 value 115.244986
## iter 270 value 113.213739
## iter 280 value 111.301315
## iter 290 value 109.924322
## iter 300 value 109.331104
## iter 310 value 108.782459
## iter 320 value 108.371454
## iter 330 value 107.589580
## iter 340 value 106.681442
## iter 350 value 106.021792
## iter 360 value 105.790937
## iter 370 value 105.658899
## iter 380 value 105.628294
## iter 390 value 105.586666
## iter 400 value 105.517853
## iter 410 value 105.416924
## iter 420 value 105.270379
## iter 430 value 105.033196
## iter 440 value 104.586130
## iter 450 value 103.719464
## iter 460 value 102.929766
## iter 470 value 102.136290
## iter 480 value 101.529458
## iter 490 value 100.703155
## iter 500 value 99.567269
## final  value 99.567269 
## stopped after 500 iterations
## # weights:  241
## initial  value 1425732.097986 
## iter  10 value 1375.640186
## iter  20 value 821.895031
## iter  30 value 674.939125
## iter  40 value 502.232512
## iter  50 value 429.798325
## iter  60 value 358.629293
## iter  70 value 307.894247
## iter  80 value 271.940674
## iter  90 value 246.675038
## iter 100 value 224.864809
## iter 110 value 209.590616
## iter 120 value 198.214014
## iter 130 value 190.841716
## iter 140 value 184.426557
## iter 150 value 176.574111
## iter 160 value 167.216698
## iter 170 value 159.219820
## iter 180 value 150.289408
## iter 190 value 142.661961
## iter 200 value 136.443674
## iter 210 value 126.965162
## iter 220 value 119.114537
## iter 230 value 113.614611
## iter 240 value 107.524755
## iter 250 value 101.507598
## iter 260 value 97.952182
## iter 270 value 94.091510
## iter 280 value 89.646154
## iter 290 value 85.841245
## iter 300 value 80.774047
## iter 310 value 74.506634
## iter 320 value 70.152405
## iter 330 value 65.298885
## iter 340 value 62.480585
## iter 350 value 59.811508
## iter 360 value 57.588691
## iter 370 value 55.438650
## iter 380 value 53.187944
## iter 390 value 50.253663
## iter 400 value 48.655191
## iter 410 value 46.851427
## iter 420 value 45.727083
## iter 430 value 44.974745
## iter 440 value 44.212167
## iter 450 value 43.374185
## iter 460 value 42.610683
## iter 470 value 42.158281
## iter 480 value 41.509030
## iter 490 value 41.182159
## iter 500 value 41.096494
## final  value 41.096494 
## stopped after 500 iterations
## # weights:  25
## initial  value 1364156.587034 
## iter  10 value 19539.964640
## iter  20 value 14785.573082
## iter  30 value 11058.799529
## iter  40 value 3463.995804
## iter  50 value 1678.490273
## iter  60 value 1297.823507
## iter  70 value 1179.016997
## iter  80 value 1173.001288
## iter  90 value 1167.044784
## iter 100 value 1148.292159
## iter 110 value 1147.931182
## final  value 1147.930339 
## converged
## # weights:  61
## initial  value 1414575.668548 
## iter  10 value 76283.602962
## iter  20 value 35031.985881
## iter  30 value 21693.498478
## iter  40 value 12984.117480
## iter  50 value 7398.826873
## iter  60 value 6909.962394
## iter  70 value 4436.751243
## iter  80 value 2952.111164
## iter  90 value 2105.011744
## iter 100 value 1449.891408
## iter 110 value 1176.898926
## iter 120 value 1092.502129
## iter 130 value 1031.599327
## iter 140 value 993.858163
## iter 150 value 956.603983
## iter 160 value 930.869144
## iter 170 value 918.121027
## iter 180 value 910.244988
## iter 190 value 892.411503
## iter 200 value 870.429695
## iter 210 value 855.490608
## iter 220 value 849.247674
## iter 230 value 846.339099
## iter 240 value 845.042226
## iter 250 value 844.976353
## iter 260 value 844.972309
## final  value 844.971867 
## converged
## # weights:  121
## initial  value 1398789.313064 
## iter  10 value 1245.524635
## iter  20 value 931.859082
## iter  30 value 795.605418
## iter  40 value 704.351808
## iter  50 value 651.861833
## iter  60 value 619.390576
## iter  70 value 592.516517
## iter  80 value 570.088424
## iter  90 value 554.220154
## iter 100 value 540.937813
## iter 110 value 528.404300
## iter 120 value 518.983216
## iter 130 value 509.965185
## iter 140 value 508.015754
## iter 150 value 506.145222
## iter 160 value 504.767414
## iter 170 value 502.899432
## iter 180 value 501.342660
## iter 190 value 498.254652
## iter 200 value 492.867182
## iter 210 value 489.031889
## iter 220 value 486.915882
## iter 230 value 485.663998
## iter 240 value 482.233454
## iter 250 value 479.707872
## iter 260 value 477.795332
## iter 270 value 474.942479
## iter 280 value 472.789943
## iter 290 value 468.435254
## iter 300 value 463.322175
## iter 310 value 460.870158
## iter 320 value 458.411777
## iter 330 value 456.307730
## iter 340 value 454.858027
## iter 350 value 452.418812
## iter 360 value 450.989034
## iter 370 value 450.433488
## iter 380 value 450.092757
## iter 390 value 449.746967
## iter 400 value 449.615295
## iter 410 value 449.575150
## iter 420 value 449.560120
## iter 430 value 449.557328
## final  value 449.557048 
## converged
## # weights:  181
## initial  value 1346723.388655 
## iter  10 value 1101.374678
## iter  20 value 864.999899
## iter  30 value 739.426692
## iter  40 value 650.496874
## iter  50 value 588.301452
## iter  60 value 555.138072
## iter  70 value 522.899716
## iter  80 value 497.945826
## iter  90 value 484.694735
## iter 100 value 469.009760
## iter 110 value 455.802350
## iter 120 value 449.051663
## iter 130 value 443.844046
## iter 140 value 439.253970
## iter 150 value 435.087032
## iter 160 value 431.127004
## iter 170 value 426.974663
## iter 180 value 420.786428
## iter 190 value 416.027188
## iter 200 value 412.809534
## iter 210 value 409.604306
## iter 220 value 404.630504
## iter 230 value 400.436890
## iter 240 value 395.725722
## iter 250 value 393.608461
## iter 260 value 390.758111
## iter 270 value 387.528131
## iter 280 value 381.836783
## iter 290 value 379.516696
## iter 300 value 376.297230
## iter 310 value 372.710206
## iter 320 value 369.615908
## iter 330 value 366.944220
## iter 340 value 365.069726
## iter 350 value 363.283517
## iter 360 value 361.330166
## iter 370 value 360.345732
## iter 380 value 359.371164
## iter 390 value 357.009300
## iter 400 value 355.587019
## iter 410 value 354.970429
## iter 420 value 354.070544
## iter 430 value 353.282444
## iter 440 value 352.916982
## iter 450 value 352.650010
## iter 460 value 352.239208
## iter 470 value 351.409474
## iter 480 value 350.727397
## iter 490 value 350.492440
## iter 500 value 350.430458
## final  value 350.430458 
## stopped after 500 iterations
## # weights:  241
## initial  value 1375346.489162 
## iter  10 value 1367.781777
## iter  20 value 952.422485
## iter  30 value 744.106819
## iter  40 value 640.112622
## iter  50 value 580.162631
## iter  60 value 534.425015
## iter  70 value 506.890845
## iter  80 value 481.737665
## iter  90 value 463.292341
## iter 100 value 449.669845
## iter 110 value 444.419172
## iter 120 value 439.458936
## iter 130 value 434.693792
## iter 140 value 429.813411
## iter 150 value 424.204475
## iter 160 value 417.521980
## iter 170 value 410.775121
## iter 180 value 402.844156
## iter 190 value 397.131327
## iter 200 value 393.128716
## iter 210 value 389.588363
## iter 220 value 385.937226
## iter 230 value 382.106459
## iter 240 value 379.076241
## iter 250 value 375.895933
## iter 260 value 372.705938
## iter 270 value 368.916919
## iter 280 value 366.265915
## iter 290 value 361.644001
## iter 300 value 357.251667
## iter 310 value 354.556305
## iter 320 value 352.597746
## iter 330 value 351.230341
## iter 340 value 349.928834
## iter 350 value 348.678311
## iter 360 value 347.004868
## iter 370 value 344.336915
## iter 380 value 342.492997
## iter 390 value 340.550433
## iter 400 value 339.090973
## iter 410 value 337.914670
## iter 420 value 337.158985
## iter 430 value 336.342028
## iter 440 value 335.526791
## iter 450 value 334.512118
## iter 460 value 333.043500
## iter 470 value 330.402574
## iter 480 value 327.984257
## iter 490 value 326.813734
## iter 500 value 325.843866
## final  value 325.843866 
## stopped after 500 iterations
## # weights:  25
## initial  value 1394525.925872 
## iter  10 value 96591.768306
## iter  20 value 16831.014352
## iter  30 value 15384.630985
## iter  40 value 15083.243450
## iter  50 value 15075.854447
## iter  60 value 15072.182372
## iter  70 value 15065.526987
## iter  80 value 12120.845998
## iter  90 value 8838.201624
## iter 100 value 5509.937832
## iter 110 value 5483.839417
## iter 120 value 5438.417239
## iter 130 value 5380.122125
## iter 140 value 4735.464660
## iter 150 value 1628.060636
## iter 160 value 1453.620316
## iter 170 value 1388.298703
## iter 180 value 1372.723301
## iter 190 value 1367.740539
## iter 200 value 1339.280814
## iter 210 value 1280.134873
## iter 220 value 1276.379470
## iter 230 value 1255.258570
## iter 240 value 1254.302727
## iter 250 value 1253.854252
## iter 260 value 1253.847075
## iter 270 value 1241.281215
## iter 280 value 1232.996042
## iter 290 value 1232.646939
## iter 300 value 1216.368372
## iter 310 value 1207.771213
## iter 320 value 1205.549531
## iter 330 value 1198.297716
## iter 340 value 1181.254359
## iter 350 value 1177.466464
## iter 360 value 1167.810985
## iter 370 value 1167.158281
## final  value 1167.157437 
## converged
## # weights:  61
## initial  value 1382301.841007 
## iter  10 value 197526.920235
## iter  20 value 9635.408165
## iter  30 value 4792.720309
## iter  40 value 3680.483923
## iter  50 value 2423.108187
## iter  60 value 1652.898777
## iter  70 value 1281.654353
## iter  80 value 1167.694244
## iter  90 value 1123.592482
## iter 100 value 1061.404972
## iter 110 value 904.521784
## iter 120 value 837.800852
## iter 130 value 804.907375
## iter 140 value 775.416852
## iter 150 value 754.587690
## iter 160 value 747.637225
## iter 170 value 729.189097
## iter 180 value 728.253221
## iter 190 value 724.846727
## iter 200 value 714.302200
## iter 210 value 710.181162
## iter 220 value 705.381564
## iter 230 value 700.195031
## iter 240 value 697.287720
## iter 250 value 694.076784
## iter 260 value 688.137707
## iter 270 value 686.072856
## iter 280 value 682.652251
## iter 290 value 676.285247
## iter 300 value 658.730408
## iter 310 value 644.137320
## iter 320 value 639.347843
## iter 330 value 636.097032
## iter 340 value 633.707720
## iter 350 value 632.936031
## iter 360 value 632.581748
## iter 370 value 632.261613
## iter 380 value 631.725310
## iter 390 value 631.661774
## iter 400 value 631.655817
## iter 410 value 631.578135
## iter 420 value 631.372563
## iter 430 value 631.095213
## iter 440 value 630.700405
## iter 450 value 625.254516
## iter 460 value 620.312404
## iter 470 value 618.656560
## iter 480 value 617.849330
## iter 490 value 617.604580
## iter 500 value 617.529045
## final  value 617.529045 
## stopped after 500 iterations
## # weights:  121
## initial  value 1409867.375222 
## iter  10 value 1686.405311
## iter  20 value 857.985567
## iter  30 value 660.476272
## iter  40 value 600.954001
## iter  50 value 527.974385
## iter  60 value 470.255145
## iter  70 value 436.553263
## iter  80 value 408.015208
## iter  90 value 390.910751
## iter 100 value 377.906162
## iter 110 value 358.840807
## iter 120 value 345.128001
## iter 130 value 341.036040
## iter 140 value 338.499667
## iter 150 value 326.182967
## iter 160 value 323.129776
## iter 170 value 321.399314
## iter 180 value 318.828754
## iter 190 value 316.899890
## iter 200 value 311.504787
## iter 210 value 308.387322
## iter 220 value 305.132693
## iter 230 value 302.722422
## iter 240 value 300.499990
## iter 250 value 299.968106
## iter 260 value 299.416763
## iter 270 value 298.681857
## iter 280 value 297.886601
## iter 290 value 296.718652
## iter 300 value 295.772848
## iter 310 value 294.240028
## iter 320 value 292.666977
## iter 330 value 291.284409
## iter 340 value 290.598466
## iter 350 value 290.141568
## iter 360 value 289.676749
## iter 370 value 289.432865
## iter 380 value 289.323966
## iter 390 value 289.283227
## iter 400 value 289.256618
## iter 410 value 289.231913
## iter 420 value 289.206883
## iter 430 value 289.196671
## iter 440 value 289.192607
## iter 450 value 289.190660
## iter 460 value 289.189607
## iter 470 value 289.189377
## final  value 289.189261 
## converged
## # weights:  181
## initial  value 1374732.323372 
## iter  10 value 993.897666
## iter  20 value 798.551150
## iter  30 value 632.626272
## iter  40 value 516.047781
## iter  50 value 413.495333
## iter  60 value 361.204829
## iter  70 value 328.198015
## iter  80 value 300.822750
## iter  90 value 282.687335
## iter 100 value 253.961233
## iter 110 value 233.658455
## iter 120 value 221.830020
## iter 130 value 212.931586
## iter 140 value 201.027050
## iter 150 value 186.088598
## iter 160 value 177.967819
## iter 170 value 170.990546
## iter 180 value 165.915141
## iter 190 value 162.703550
## iter 200 value 159.956373
## iter 210 value 156.920766
## iter 220 value 154.663669
## iter 230 value 152.834656
## iter 240 value 150.409344
## iter 250 value 148.873334
## iter 260 value 147.628046
## iter 270 value 146.291252
## iter 280 value 145.285251
## iter 290 value 144.403643
## iter 300 value 143.158265
## iter 310 value 141.894921
## iter 320 value 141.327458
## iter 330 value 140.703712
## iter 340 value 139.975127
## iter 350 value 138.948636
## iter 360 value 138.546029
## iter 370 value 138.380350
## iter 380 value 138.331668
## iter 390 value 138.226743
## iter 400 value 138.074051
## iter 410 value 137.916661
## iter 420 value 137.626705
## iter 430 value 137.289046
## iter 440 value 136.764157
## iter 450 value 136.158504
## iter 460 value 135.157178
## iter 470 value 134.636990
## iter 480 value 134.258048
## iter 490 value 134.019353
## iter 500 value 133.541902
## final  value 133.541902 
## stopped after 500 iterations
## # weights:  241
## initial  value 1420191.736214 
## iter  10 value 2057.734051
## iter  20 value 894.692453
## iter  30 value 685.520099
## iter  40 value 536.892384
## iter  50 value 376.024034
## iter  60 value 281.758903
## iter  70 value 228.054783
## iter  80 value 199.570915
## iter  90 value 163.913101
## iter 100 value 144.275504
## iter 110 value 131.106369
## iter 120 value 121.123848
## iter 130 value 114.278448
## iter 140 value 108.241403
## iter 150 value 103.128019
## iter 160 value 99.254677
## iter 170 value 94.806304
## iter 180 value 92.242950
## iter 190 value 90.153653
## iter 200 value 88.112457
## iter 210 value 85.922383
## iter 220 value 83.753258
## iter 230 value 82.251870
## iter 240 value 81.010004
## iter 250 value 79.021711
## iter 260 value 76.859333
## iter 270 value 74.715342
## iter 280 value 71.674523
## iter 290 value 68.532800
## iter 300 value 66.781845
## iter 310 value 65.937599
## iter 320 value 65.181016
## iter 330 value 64.670816
## iter 340 value 64.179231
## iter 350 value 63.737232
## iter 360 value 63.245710
## iter 370 value 62.682823
## iter 380 value 62.107532
## iter 390 value 61.398817
## iter 400 value 60.867428
## iter 410 value 60.508152
## iter 420 value 60.185347
## iter 430 value 59.828264
## iter 440 value 59.442172
## iter 450 value 58.960882
## iter 460 value 58.458292
## iter 470 value 57.814036
## iter 480 value 56.972497
## iter 490 value 56.573496
## iter 500 value 56.443051
## final  value 56.443051 
## stopped after 500 iterations
## # weights:  25
## initial  value 1398875.043009 
## iter  10 value 8819.870440
## iter  20 value 5882.813063
## iter  30 value 5507.897629
## iter  40 value 5451.451290
## iter  50 value 5403.953066
## iter  60 value 5330.115179
## iter  70 value 5110.688737
## iter  80 value 4432.810618
## iter  90 value 4070.066173
## iter 100 value 3279.990254
## iter 110 value 2551.505919
## iter 120 value 1604.510803
## iter 130 value 1329.459412
## iter 140 value 1286.806778
## iter 150 value 1273.692606
## iter 160 value 1264.217897
## iter 170 value 1238.732191
## iter 180 value 1224.929641
## iter 190 value 1220.530398
## iter 200 value 1218.663529
## iter 210 value 1218.591887
## iter 220 value 1218.241435
## iter 230 value 1216.480308
## iter 240 value 1203.749134
## iter 250 value 1146.453873
## iter 260 value 1146.317447
## iter 270 value 1145.212039
## iter 280 value 1145.068756
## iter 290 value 1142.160198
## iter 300 value 1139.798014
## iter 310 value 1136.672279
## iter 320 value 1136.263102
## iter 330 value 1135.616945
## iter 340 value 1135.514163
## iter 350 value 1134.582958
## iter 360 value 1134.153595
## iter 370 value 1134.149932
## iter 380 value 1133.942022
## iter 390 value 1133.928792
## iter 400 value 1132.141585
## iter 410 value 1131.614865
## iter 420 value 1126.958464
## iter 430 value 1125.260285
## iter 440 value 1124.952637
## iter 450 value 1118.158286
## iter 460 value 1111.113245
## iter 470 value 1101.413731
## iter 480 value 1062.196960
## iter 490 value 1023.187145
## iter 500 value 1017.431136
## final  value 1017.431136 
## stopped after 500 iterations
## # weights:  61
## initial  value 1383047.417602 
## iter  10 value 16099.374415
## iter  20 value 11548.531358
## iter  30 value 10546.615096
## iter  40 value 7370.107431
## iter  50 value 3578.066733
## iter  60 value 1626.687570
## iter  70 value 1197.689323
## iter  80 value 1108.770595
## iter  90 value 1012.561383
## iter 100 value 955.221352
## iter 110 value 907.183952
## iter 120 value 871.015366
## iter 130 value 856.692499
## iter 140 value 853.396985
## iter 150 value 845.333414
## iter 160 value 829.640436
## iter 170 value 807.567785
## iter 180 value 792.420814
## iter 190 value 782.573851
## iter 200 value 779.102500
## iter 210 value 777.553948
## iter 220 value 769.918506
## iter 230 value 735.339537
## iter 240 value 714.457560
## iter 250 value 698.699287
## iter 260 value 691.279157
## iter 270 value 688.674901
## iter 280 value 677.039304
## iter 290 value 631.340602
## iter 300 value 613.897297
## iter 310 value 611.082670
## iter 320 value 607.720489
## iter 330 value 606.323803
## iter 340 value 603.018002
## iter 350 value 602.708741
## iter 360 value 600.629701
## iter 370 value 598.185695
## iter 380 value 596.872689
## iter 390 value 592.740234
## iter 400 value 589.894572
## iter 410 value 587.742005
## iter 420 value 586.965381
## iter 430 value 586.584818
## iter 440 value 586.486450
## iter 450 value 586.461489
## iter 460 value 586.432626
## iter 470 value 586.430373
## iter 480 value 586.428895
## iter 490 value 586.424850
## iter 500 value 586.421392
## final  value 586.421392 
## stopped after 500 iterations
## # weights:  121
## initial  value 1370582.836008 
## iter  10 value 3631.614799
## iter  20 value 1461.785218
## iter  30 value 937.099507
## iter  40 value 726.762550
## iter  50 value 639.689765
## iter  60 value 583.205764
## iter  70 value 541.290586
## iter  80 value 524.292339
## iter  90 value 506.170388
## iter 100 value 483.930230
## iter 110 value 468.250542
## iter 120 value 454.600133
## iter 130 value 444.927968
## iter 140 value 434.938408
## iter 150 value 413.801984
## iter 160 value 399.180750
## iter 170 value 388.089136
## iter 180 value 359.774126
## iter 190 value 343.964155
## iter 200 value 336.981816
## iter 210 value 329.543988
## iter 220 value 322.998963
## iter 230 value 318.702750
## iter 240 value 312.643871
## iter 250 value 310.259713
## iter 260 value 309.329466
## iter 270 value 307.907810
## iter 280 value 304.785206
## iter 290 value 301.788219
## iter 300 value 298.910270
## iter 310 value 295.418393
## iter 320 value 292.159920
## iter 330 value 287.965339
## iter 340 value 284.646988
## iter 350 value 283.199269
## iter 360 value 281.989772
## iter 370 value 280.749280
## iter 380 value 279.565748
## iter 390 value 278.979001
## iter 400 value 278.033289
## iter 410 value 277.816894
## iter 420 value 277.439210
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## iter 440 value 275.728835
## iter 450 value 274.547547
## iter 460 value 273.365326
## iter 470 value 272.185985
## iter 480 value 271.001493
## iter 490 value 270.570757
## iter 500 value 270.540563
## final  value 270.540563 
## stopped after 500 iterations
## # weights:  181
## initial  value 1434633.464253 
## iter  10 value 1211.061852
## iter  20 value 740.764855
## iter  30 value 616.856084
## iter  40 value 503.382613
## iter  50 value 410.970976
## iter  60 value 375.439027
## iter  70 value 332.738887
## iter  80 value 301.945400
## iter  90 value 276.229714
## iter 100 value 250.791289
## iter 110 value 238.606187
## iter 120 value 230.151814
## iter 130 value 223.046048
## iter 140 value 213.432032
## iter 150 value 197.804502
## iter 160 value 184.769927
## iter 170 value 170.691073
## iter 180 value 162.820412
## iter 190 value 156.479168
## iter 200 value 151.290284
## iter 210 value 141.022906
## iter 220 value 126.075834
## iter 230 value 116.245571
## iter 240 value 110.216605
## iter 250 value 104.471068
## iter 260 value 100.209819
## iter 270 value 97.100936
## iter 280 value 95.053595
## iter 290 value 92.837929
## iter 300 value 91.934768
## iter 310 value 91.437807
## iter 320 value 91.062259
## iter 330 value 90.605726
## iter 340 value 89.903375
## iter 350 value 88.760969
## iter 360 value 87.863546
## iter 370 value 87.373861
## iter 380 value 87.185966
## iter 390 value 86.984858
## iter 400 value 86.657550
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## iter 430 value 85.686299
## iter 440 value 85.077950
## iter 450 value 84.542381
## iter 460 value 84.062794
## iter 470 value 83.050730
## iter 480 value 81.595828
## iter 490 value 80.621371
## iter 500 value 80.021326
## final  value 80.021326 
## stopped after 500 iterations
## # weights:  241
## initial  value 1380919.724748 
## iter  10 value 1184.693603
## iter  20 value 789.846203
## iter  30 value 626.099619
## iter  40 value 513.622058
## iter  50 value 405.533747
## iter  60 value 352.905578
## iter  70 value 311.974266
## iter  80 value 275.686075
## iter  90 value 242.976254
## iter 100 value 219.749898
## iter 110 value 208.264133
## iter 120 value 197.900302
## iter 130 value 187.230331
## iter 140 value 177.421524
## iter 150 value 165.264409
## iter 160 value 156.051381
## iter 170 value 146.881022
## iter 180 value 135.562500
## iter 190 value 124.040259
## iter 200 value 113.150708
## iter 210 value 102.619258
## iter 220 value 97.142019
## iter 230 value 94.396963
## iter 240 value 90.432377
## iter 250 value 85.656531
## iter 260 value 81.011204
## iter 270 value 76.749862
## iter 280 value 72.769221
## iter 290 value 70.030147
## iter 300 value 68.263905
## iter 310 value 67.135401
## iter 320 value 65.979457
## iter 330 value 63.174203
## iter 340 value 60.680648
## iter 350 value 58.673483
## iter 360 value 57.193236
## iter 370 value 56.128040
## iter 380 value 55.409464
## iter 390 value 54.548465
## iter 400 value 53.887770
## iter 410 value 53.379384
## iter 420 value 52.784614
## iter 430 value 51.666369
## iter 440 value 51.084932
## iter 450 value 49.981722
## iter 460 value 49.122090
## iter 470 value 48.239312
## iter 480 value 47.552507
## iter 490 value 47.350896
## iter 500 value 47.315523
## final  value 47.315523 
## stopped after 500 iterations
## # weights:  25
## initial  value 1381291.702288 
## iter  10 value 19170.296626
## iter  20 value 10929.812398
## iter  30 value 7897.891611
## iter  40 value 6679.587140
## iter  50 value 6334.766106
## iter  60 value 6013.615421
## iter  70 value 5780.300317
## iter  80 value 5769.670774
## iter  90 value 5748.883112
## iter 100 value 4648.913394
## iter 110 value 3996.047390
## iter 120 value 3018.082634
## iter 130 value 1460.402361
## iter 140 value 1232.551252
## iter 150 value 1195.956300
## iter 160 value 1192.217119
## iter 170 value 1169.971604
## iter 180 value 1148.746503
## iter 190 value 1147.355879
## iter 200 value 1146.382529
## iter 210 value 1146.337208
## iter 220 value 1146.196806
## iter 230 value 1145.613721
## iter 240 value 1143.586743
## iter 250 value 1141.448504
## iter 260 value 1141.040413
## iter 270 value 1139.757698
## iter 280 value 1139.181993
## iter 290 value 1139.143473
## final  value 1139.143279 
## converged
## # weights:  61
## initial  value 1419395.360927 
## iter  10 value 186509.571527
## iter  20 value 7215.249148
## iter  30 value 5607.241744
## iter  40 value 5032.049500
## iter  50 value 4271.261031
## iter  60 value 4088.247275
## iter  70 value 4029.045672
## iter  80 value 3970.924417
## iter  90 value 3856.017163
## iter 100 value 3809.492153
## iter 110 value 3622.428748
## iter 120 value 3057.903696
## iter 130 value 2809.161917
## iter 140 value 2416.262927
## iter 150 value 1820.058290
## iter 160 value 1382.820839
## iter 170 value 1179.226401
## iter 180 value 1115.911817
## iter 190 value 1042.525511
## iter 200 value 940.005258
## iter 210 value 909.066095
## iter 220 value 881.033606
## iter 230 value 872.761878
## iter 240 value 868.823722
## iter 250 value 861.873725
## iter 260 value 855.749084
## iter 270 value 844.754110
## iter 280 value 826.804347
## iter 290 value 816.653710
## iter 300 value 816.018365
## iter 310 value 814.807838
## iter 320 value 812.014330
## iter 330 value 809.141530
## iter 340 value 806.424473
## iter 350 value 800.620058
## iter 360 value 794.993656
## iter 370 value 791.467226
## iter 380 value 786.121669
## iter 390 value 783.201729
## iter 400 value 774.259017
## iter 410 value 766.611068
## iter 420 value 765.104053
## iter 430 value 763.090335
## iter 440 value 753.495200
## iter 450 value 741.318883
## iter 460 value 727.764487
## iter 470 value 717.041868
## iter 480 value 713.670614
## iter 490 value 708.424507
## iter 500 value 703.601768
## final  value 703.601768 
## stopped after 500 iterations
## # weights:  121
## initial  value 1429577.952893 
## iter  10 value 2390.312660
## iter  20 value 1072.965842
## iter  30 value 818.206099
## iter  40 value 735.713867
## iter  50 value 699.178663
## iter  60 value 660.249254
## iter  70 value 612.585915
## iter  80 value 574.217161
## iter  90 value 521.422068
## iter 100 value 491.239908
## iter 110 value 473.082935
## iter 120 value 459.193485
## iter 130 value 448.096033
## iter 140 value 435.063752
## iter 150 value 429.396397
## iter 160 value 417.450516
## iter 170 value 402.722272
## iter 180 value 396.715428
## iter 190 value 385.287647
## iter 200 value 375.879126
## iter 210 value 369.548957
## iter 220 value 367.361667
## iter 230 value 366.432644
## iter 240 value 365.834021
## iter 250 value 364.705501
## iter 260 value 364.063618
## iter 270 value 363.964345
## iter 280 value 363.935004
## iter 290 value 363.909961
## iter 300 value 363.810366
## iter 310 value 363.651256
## iter 320 value 363.617918
## iter 330 value 363.599836
## iter 340 value 363.558064
## iter 350 value 363.507149
## iter 360 value 363.500336
## iter 370 value 363.495243
## iter 380 value 363.489160
## iter 390 value 363.474370
## iter 400 value 363.446190
## iter 410 value 363.419429
## iter 420 value 363.394261
## iter 430 value 363.361176
## iter 440 value 363.252267
## iter 450 value 363.150435
## iter 460 value 363.086528
## iter 470 value 363.067940
## iter 480 value 363.057312
## iter 490 value 363.031294
## iter 500 value 362.991097
## final  value 362.991097 
## stopped after 500 iterations
## # weights:  181
## initial  value 1350755.748260 
## iter  10 value 1220.402385
## iter  20 value 763.100713
## iter  30 value 633.896971
## iter  40 value 466.452598
## iter  50 value 399.771149
## iter  60 value 368.036792
## iter  70 value 329.372157
## iter  80 value 293.813323
## iter  90 value 271.838244
## iter 100 value 254.766132
## iter 110 value 242.285724
## iter 120 value 232.185933
## iter 130 value 216.780280
## iter 140 value 203.960260
## iter 150 value 193.601419
## iter 160 value 187.790115
## iter 170 value 181.652251
## iter 180 value 175.460822
## iter 190 value 171.930011
## iter 200 value 167.889453
## iter 210 value 158.345608
## iter 220 value 148.979145
## iter 230 value 142.139185
## iter 240 value 135.812465
## iter 250 value 130.878443
## iter 260 value 127.625020
## iter 270 value 125.426829
## iter 280 value 122.180578
## iter 290 value 117.960889
## iter 300 value 115.152527
## iter 310 value 112.582094
## iter 320 value 110.277962
## iter 330 value 108.767532
## iter 340 value 106.006354
## iter 350 value 103.695158
## iter 360 value 101.830291
## iter 370 value 100.757616
## iter 380 value 100.326323
## iter 390 value 99.696350
## iter 400 value 98.753795
## iter 410 value 97.407699
## iter 420 value 95.780780
## iter 430 value 93.485933
## iter 440 value 91.148077
## iter 450 value 89.085172
## iter 460 value 86.046780
## iter 470 value 81.820142
## iter 480 value 76.655161
## iter 490 value 74.604135
## iter 500 value 72.456178
## final  value 72.456178 
## stopped after 500 iterations
## # weights:  241
## initial  value 1362495.416274 
## iter  10 value 1158.839226
## iter  20 value 766.560537
## iter  30 value 623.058648
## iter  40 value 514.301309
## iter  50 value 427.288347
## iter  60 value 347.916089
## iter  70 value 307.047745
## iter  80 value 273.553583
## iter  90 value 254.909816
## iter 100 value 227.994250
## iter 110 value 202.027139
## iter 120 value 185.216303
## iter 130 value 171.301744
## iter 140 value 161.649326
## iter 150 value 154.450584
## iter 160 value 147.610851
## iter 170 value 140.130007
## iter 180 value 133.286893
## iter 190 value 124.767430
## iter 200 value 115.677226
## iter 210 value 106.998846
## iter 220 value 98.463812
## iter 230 value 93.007201
## iter 240 value 86.658881
## iter 250 value 82.328241
## iter 260 value 79.060513
## iter 270 value 76.524798
## iter 280 value 73.733815
## iter 290 value 70.893231
## iter 300 value 68.572904
## iter 310 value 66.444971
## iter 320 value 64.926784
## iter 330 value 62.693781
## iter 340 value 60.601489
## iter 350 value 58.470096
## iter 360 value 56.653475
## iter 370 value 54.338856
## iter 380 value 52.034366
## iter 390 value 49.972294
## iter 400 value 47.833071
## iter 410 value 46.014835
## iter 420 value 43.722277
## iter 430 value 41.594130
## iter 440 value 39.594202
## iter 450 value 37.415070
## iter 460 value 35.317576
## iter 470 value 34.456194
## iter 480 value 33.682148
## iter 490 value 33.396856
## iter 500 value 33.290554
## final  value 33.290554 
## stopped after 500 iterations
## # weights:  25
## initial  value 1352804.101603 
## iter  10 value 7275.747305
## iter  20 value 5671.697933
## iter  30 value 4972.380138
## iter  40 value 3179.032765
## iter  50 value 1704.377016
## iter  60 value 1450.697286
## iter  70 value 1432.154930
## iter  80 value 1354.504384
## iter  90 value 1336.069149
## iter 100 value 1329.949531
## iter 110 value 1327.451469
## iter 120 value 1327.351137
## iter 130 value 1325.226789
## iter 140 value 1323.396121
## iter 150 value 1322.155002
## iter 160 value 1321.358865
## iter 170 value 1321.326107
## final  value 1321.326029 
## converged
## # weights:  61
## initial  value 1381519.965639 
## iter  10 value 156745.089010
## iter  20 value 19165.135566
## iter  30 value 4707.429300
## iter  40 value 2910.576358
## iter  50 value 2507.757296
## iter  60 value 2442.203789
## iter  70 value 2400.313853
## iter  80 value 2392.664782
## iter  90 value 2380.635613
## iter 100 value 2325.955800
## iter 110 value 2199.569931
## iter 120 value 1817.425136
## iter 130 value 1391.646552
## iter 140 value 1254.540213
## iter 150 value 1219.271583
## iter 160 value 1198.840266
## iter 170 value 1174.080006
## iter 180 value 1172.475059
## iter 190 value 1171.034220
## iter 200 value 1170.555707
## iter 210 value 1170.345513
## iter 220 value 1170.212457
## final  value 1170.212293 
## converged
## # weights:  121
## initial  value 1413421.109585 
## iter  10 value 1164.292169
## iter  20 value 917.817139
## iter  30 value 794.873953
## iter  40 value 704.864810
## iter  50 value 645.498750
## iter  60 value 599.702107
## iter  70 value 534.997492
## iter  80 value 502.120381
## iter  90 value 474.354445
## iter 100 value 456.554333
## iter 110 value 429.374554
## iter 120 value 393.297491
## iter 130 value 370.217467
## iter 140 value 351.341173
## iter 150 value 339.785544
## iter 160 value 331.455091
## iter 170 value 321.697691
## iter 180 value 310.101631
## iter 190 value 297.704618
## iter 200 value 288.833757
## iter 210 value 279.004290
## iter 220 value 271.420859
## iter 230 value 266.658103
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## iter 250 value 258.717112
## iter 260 value 257.455015
## iter 270 value 256.695570
## iter 280 value 254.898675
## iter 290 value 253.070971
## iter 300 value 251.184672
## iter 310 value 249.060118
## iter 320 value 246.855712
## iter 330 value 244.456969
## iter 340 value 241.698852
## iter 350 value 239.301865
## iter 360 value 236.819552
## iter 370 value 235.800689
## iter 380 value 235.176343
## iter 390 value 234.545566
## iter 400 value 234.206551
## iter 410 value 233.947747
## iter 420 value 233.490559
## iter 430 value 232.665740
## iter 440 value 231.071099
## iter 450 value 229.779929
## iter 460 value 226.675373
## iter 470 value 225.458197
## iter 480 value 224.716210
## iter 490 value 224.045071
## iter 500 value 223.982740
## final  value 223.982740 
## stopped after 500 iterations
## # weights:  181
## initial  value 1401015.855766 
## iter  10 value 1199.275633
## iter  20 value 835.197898
## iter  30 value 667.544267
## iter  40 value 529.879974
## iter  50 value 442.998801
## iter  60 value 394.055870
## iter  70 value 339.969879
## iter  80 value 309.182838
## iter  90 value 282.462925
## iter 100 value 261.871797
## iter 110 value 246.757338
## iter 120 value 233.521851
## iter 130 value 225.709182
## iter 140 value 216.937743
## iter 150 value 207.225850
## iter 160 value 196.613876
## iter 170 value 189.384304
## iter 180 value 182.776166
## iter 190 value 174.835674
## iter 200 value 169.414834
## iter 210 value 165.400080
## iter 220 value 162.083955
## iter 230 value 159.058170
## iter 240 value 155.684739
## iter 250 value 150.590213
## iter 260 value 147.163452
## iter 270 value 144.798286
## iter 280 value 141.196879
## iter 290 value 139.373404
## iter 300 value 137.832535
## iter 310 value 136.166298
## iter 320 value 134.235930
## iter 330 value 131.245240
## iter 340 value 124.752549
## iter 350 value 120.818687
## iter 360 value 117.970698
## iter 370 value 116.948100
## iter 380 value 116.534072
## iter 390 value 115.951792
## iter 400 value 115.089880
## iter 410 value 114.442252
## iter 420 value 114.124754
## iter 430 value 113.681684
## iter 440 value 113.234187
## iter 450 value 112.707178
## iter 460 value 112.293287
## iter 470 value 111.870424
## iter 480 value 110.661587
## iter 490 value 107.888475
## iter 500 value 105.202963
## final  value 105.202963 
## stopped after 500 iterations
## # weights:  241
## initial  value 1347790.223486 
## iter  10 value 1274.694409
## iter  20 value 808.576311
## iter  30 value 648.638270
## iter  40 value 536.287785
## iter  50 value 385.116096
## iter  60 value 318.603221
## iter  70 value 260.793058
## iter  80 value 221.822935
## iter  90 value 192.716942
## iter 100 value 173.013705
## iter 110 value 155.943760
## iter 120 value 141.480877
## iter 130 value 132.621339
## iter 140 value 125.795299
## iter 150 value 118.299518
## iter 160 value 108.361337
## iter 170 value 99.629122
## iter 180 value 94.671734
## iter 190 value 90.431288
## iter 200 value 87.952221
## iter 210 value 85.894707
## iter 220 value 83.802836
## iter 230 value 81.958632
## iter 240 value 79.765769
## iter 250 value 77.144538
## iter 260 value 74.585932
## iter 270 value 73.050789
## iter 280 value 71.046833
## iter 290 value 69.264372
## iter 300 value 66.930977
## iter 310 value 64.682696
## iter 320 value 62.481003
## iter 330 value 59.675131
## iter 340 value 57.580304
## iter 350 value 55.487808
## iter 360 value 53.851191
## iter 370 value 52.370625
## iter 380 value 50.969494
## iter 390 value 49.219205
## iter 400 value 48.093818
## iter 410 value 47.048995
## iter 420 value 45.992675
## iter 430 value 44.750350
## iter 440 value 43.802829
## iter 450 value 42.887897
## iter 460 value 41.846880
## iter 470 value 41.190365
## iter 480 value 40.540935
## iter 490 value 40.315329
## iter 500 value 40.266483
## final  value 40.266483 
## stopped after 500 iterations
## # weights:  25
## initial  value 1401025.775953 
## iter  10 value 11043.404590
## iter  20 value 6905.338699
## iter  30 value 5070.199120
## iter  40 value 3761.324029
## iter  50 value 2309.328186
## iter  60 value 1791.106287
## iter  70 value 1480.559083
## iter  80 value 1386.729777
## iter  90 value 1307.764525
## iter 100 value 1215.603654
## iter 110 value 1178.236964
## iter 120 value 1176.015066
## iter 130 value 1175.123844
## iter 140 value 1172.427184
## final  value 1172.395952 
## converged
## # weights:  61
## initial  value 1404180.903774 
## iter  10 value 31573.502817
## iter  20 value 13231.596346
## iter  30 value 7399.407160
## iter  40 value 5159.434897
## iter  50 value 4497.743774
## iter  60 value 3930.768539
## iter  70 value 3124.321637
## iter  80 value 2439.457839
## iter  90 value 2009.434616
## iter 100 value 1573.486539
## iter 110 value 1357.006407
## iter 120 value 1140.361910
## iter 130 value 1019.760235
## iter 140 value 963.360855
## iter 150 value 930.098693
## iter 160 value 908.354357
## iter 170 value 888.272008
## iter 180 value 881.296389
## iter 190 value 876.695459
## iter 200 value 874.615451
## iter 210 value 870.363023
## iter 220 value 838.470595
## iter 230 value 835.049599
## iter 240 value 833.398671
## iter 250 value 831.916336
## iter 260 value 831.420624
## iter 270 value 831.317426
## iter 280 value 831.257805
## final  value 831.255576 
## converged
## # weights:  121
## initial  value 1370782.628932 
## iter  10 value 1835.185494
## iter  20 value 1182.742831
## iter  30 value 891.547420
## iter  40 value 816.593527
## iter  50 value 743.507320
## iter  60 value 698.062941
## iter  70 value 671.555344
## iter  80 value 655.315633
## iter  90 value 637.368157
## iter 100 value 621.149274
## iter 110 value 605.459192
## iter 120 value 591.140128
## iter 130 value 582.825726
## iter 140 value 574.736895
## iter 150 value 569.321025
## iter 160 value 565.440523
## iter 170 value 561.799353
## iter 180 value 558.674189
## iter 190 value 557.034366
## iter 200 value 555.502303
## iter 210 value 553.398124
## iter 220 value 551.024506
## iter 230 value 545.203907
## iter 240 value 540.828656
## iter 250 value 539.351522
## iter 260 value 537.898756
## iter 270 value 535.629435
## iter 280 value 533.517504
## iter 290 value 531.924612
## iter 300 value 531.438415
## iter 310 value 531.235964
## iter 320 value 531.033193
## iter 330 value 530.879238
## iter 340 value 530.853299
## iter 350 value 530.849633
## final  value 530.849296 
## converged
## # weights:  181
## initial  value 1396669.835293 
## iter  10 value 1459.249337
## iter  20 value 943.847268
## iter  30 value 764.218153
## iter  40 value 688.685007
## iter  50 value 639.392328
## iter  60 value 590.900548
## iter  70 value 547.721395
## iter  80 value 517.740560
## iter  90 value 500.794098
## iter 100 value 490.518404
## iter 110 value 482.730854
## iter 120 value 476.569548
## iter 130 value 468.086292
## iter 140 value 462.251333
## iter 150 value 455.060492
## iter 160 value 449.760458
## iter 170 value 445.160487
## iter 180 value 437.194820
## iter 190 value 432.797278
## iter 200 value 430.241693
## iter 210 value 428.642115
## iter 220 value 426.887464
## iter 230 value 425.414867
## iter 240 value 423.880808
## iter 250 value 423.040186
## iter 260 value 422.280217
## iter 270 value 421.240249
## iter 280 value 420.300563
## iter 290 value 419.523383
## iter 300 value 418.868494
## iter 310 value 418.280813
## iter 320 value 417.845368
## iter 330 value 417.204688
## iter 340 value 416.610410
## iter 350 value 416.282281
## iter 360 value 416.137726
## iter 370 value 416.057895
## iter 380 value 415.878383
## iter 390 value 415.666092
## iter 400 value 415.582978
## iter 410 value 415.530885
## iter 420 value 415.495366
## iter 430 value 415.486320
## iter 440 value 415.483473
## iter 450 value 415.482621
## final  value 415.482481 
## converged
## # weights:  241
## initial  value 1352823.074906 
## iter  10 value 1364.247391
## iter  20 value 944.548855
## iter  30 value 767.862014
## iter  40 value 652.839381
## iter  50 value 551.724887
## iter  60 value 499.355724
## iter  70 value 473.834882
## iter  80 value 452.820290
## iter  90 value 430.623398
## iter 100 value 414.948774
## iter 110 value 406.391932
## iter 120 value 398.320012
## iter 130 value 392.456334
## iter 140 value 387.564611
## iter 150 value 384.748525
## iter 160 value 380.359562
## iter 170 value 376.127466
## iter 180 value 372.548326
## iter 190 value 370.233155
## iter 200 value 368.112323
## iter 210 value 365.701135
## iter 220 value 362.817130
## iter 230 value 360.203970
## iter 240 value 357.282967
## iter 250 value 355.076701
## iter 260 value 353.457357
## iter 270 value 352.071116
## iter 280 value 350.532779
## iter 290 value 349.377216
## iter 300 value 348.477559
## iter 310 value 347.176138
## iter 320 value 345.889497
## iter 330 value 344.609580
## iter 340 value 343.327035
## iter 350 value 342.083699
## iter 360 value 340.756455
## iter 370 value 339.689681
## iter 380 value 338.793105
## iter 390 value 338.143706
## iter 400 value 337.526551
## iter 410 value 336.930599
## iter 420 value 336.412128
## iter 430 value 336.019886
## iter 440 value 335.723649
## iter 450 value 335.419502
## iter 460 value 335.105713
## iter 470 value 334.712651
## iter 480 value 334.293680
## iter 490 value 334.134493
## iter 500 value 334.053183
## final  value 334.053183 
## stopped after 500 iterations
## # weights:  25
## initial  value 1371501.393570 
## iter  10 value 15812.800109
## iter  20 value 14498.852704
## iter  30 value 11509.286606
## iter  40 value 5363.631713
## iter  50 value 3885.058771
## iter  60 value 1717.533250
## iter  70 value 1478.854121
## iter  80 value 1390.036701
## iter  90 value 1360.228724
## iter 100 value 1349.258718
## iter 110 value 1337.154358
## iter 120 value 1336.573294
## iter 130 value 1333.436117
## iter 140 value 1319.265144
## iter 150 value 1239.333849
## iter 160 value 1201.120240
## iter 170 value 1169.830973
## iter 180 value 1165.223702
## iter 190 value 1165.175369
## iter 200 value 1165.173977
## iter 210 value 1165.103978
## iter 220 value 1165.044360
## iter 230 value 1165.033940
## final  value 1165.033793 
## converged
## # weights:  61
## initial  value 1367512.781787 
## iter  10 value 5624.713892
## iter  20 value 3454.159861
## iter  30 value 3252.868654
## iter  40 value 3123.240549
## iter  50 value 3070.193976
## iter  60 value 3028.540492
## iter  70 value 2922.773532
## iter  80 value 2758.392306
## iter  90 value 2498.258960
## iter 100 value 2149.398314
## iter 110 value 1417.310793
## iter 120 value 1278.139285
## iter 130 value 1234.033903
## iter 140 value 1217.111818
## iter 150 value 1209.421563
## iter 160 value 1168.270417
## iter 170 value 1069.780277
## iter 180 value 886.425343
## iter 190 value 872.826472
## iter 200 value 870.469876
## iter 210 value 869.489018
## iter 220 value 865.489005
## iter 230 value 861.376263
## iter 240 value 860.131048
## iter 250 value 858.754564
## iter 260 value 849.843282
## iter 270 value 730.491024
## iter 280 value 698.628076
## iter 290 value 685.949407
## iter 300 value 683.852151
## iter 310 value 683.489410
## iter 320 value 670.482992
## iter 330 value 662.575406
## iter 340 value 654.561138
## iter 350 value 653.429103
## iter 360 value 652.495234
## iter 370 value 651.839099
## iter 380 value 651.237519
## iter 390 value 646.601274
## iter 400 value 646.108265
## iter 410 value 646.037710
## iter 420 value 646.008244
## iter 430 value 645.991714
## iter 440 value 645.986599
## final  value 645.986347 
## converged
## # weights:  121
## initial  value 1400728.429412 
## iter  10 value 1364.706513
## iter  20 value 874.888205
## iter  30 value 768.707568
## iter  40 value 616.102243
## iter  50 value 540.095203
## iter  60 value 472.917485
## iter  70 value 441.599159
## iter  80 value 416.822087
## iter  90 value 403.795130
## iter 100 value 396.867524
## iter 110 value 388.455958
## iter 120 value 380.492912
## iter 130 value 371.013711
## iter 140 value 365.262408
## iter 150 value 360.761071
## iter 160 value 355.991708
## iter 170 value 352.402335
## iter 180 value 349.361904
## iter 190 value 347.035457
## iter 200 value 343.100514
## iter 210 value 336.608525
## iter 220 value 332.409887
## iter 230 value 324.236699
## iter 240 value 319.264667
## iter 250 value 316.547188
## iter 260 value 315.580287
## iter 270 value 314.429761
## iter 280 value 313.350681
## iter 290 value 310.104594
## iter 300 value 303.564755
## iter 310 value 296.815682
## iter 320 value 294.366213
## iter 330 value 291.735325
## iter 340 value 288.748958
## iter 350 value 286.346441
## iter 360 value 284.485142
## iter 370 value 282.677060
## iter 380 value 280.912126
## iter 390 value 279.239437
## iter 400 value 274.843544
## iter 410 value 272.397750
## iter 420 value 271.825284
## iter 430 value 271.346374
## iter 440 value 271.102669
## iter 450 value 270.982241
## iter 460 value 270.863812
## iter 470 value 270.762054
## iter 480 value 270.516961
## iter 490 value 270.194121
## iter 500 value 270.155559
## final  value 270.155559 
## stopped after 500 iterations
## # weights:  181
## initial  value 1355913.169803 
## iter  10 value 1214.270392
## iter  20 value 883.564134
## iter  30 value 700.887629
## iter  40 value 540.335990
## iter  50 value 421.797467
## iter  60 value 369.981514
## iter  70 value 331.587285
## iter  80 value 286.309146
## iter  90 value 245.498765
## iter 100 value 220.977403
## iter 110 value 208.209116
## iter 120 value 202.022162
## iter 130 value 193.832690
## iter 140 value 188.154214
## iter 150 value 179.154988
## iter 160 value 171.998335
## iter 170 value 165.404896
## iter 180 value 159.495857
## iter 190 value 153.729249
## iter 200 value 147.000568
## iter 210 value 142.688367
## iter 220 value 139.452120
## iter 230 value 137.143433
## iter 240 value 135.120110
## iter 250 value 133.111719
## iter 260 value 131.704885
## iter 270 value 130.268911
## iter 280 value 129.509212
## iter 290 value 128.682337
## iter 300 value 128.188186
## iter 310 value 127.838772
## iter 320 value 127.587913
## iter 330 value 127.392050
## iter 340 value 127.264736
## iter 350 value 127.089484
## iter 360 value 126.644431
## iter 370 value 126.328975
## iter 380 value 126.153411
## iter 390 value 125.990784
## iter 400 value 125.892351
## iter 410 value 125.656004
## iter 420 value 125.241357
## iter 430 value 124.689348
## iter 440 value 123.753283
## iter 450 value 122.737844
## iter 460 value 121.731978
## iter 470 value 120.928311
## iter 480 value 120.271322
## iter 490 value 119.749593
## iter 500 value 119.341751
## final  value 119.341751 
## stopped after 500 iterations
## # weights:  241
## initial  value 1437169.794289 
## iter  10 value 1017.666682
## iter  20 value 778.544960
## iter  30 value 656.124445
## iter  40 value 532.577654
## iter  50 value 407.825427
## iter  60 value 333.251616
## iter  70 value 275.905876
## iter  80 value 240.902671
## iter  90 value 221.447792
## iter 100 value 199.164324
## iter 110 value 189.348405
## iter 120 value 176.492271
## iter 130 value 160.507105
## iter 140 value 146.923815
## iter 150 value 137.611776
## iter 160 value 130.714043
## iter 170 value 125.245865
## iter 180 value 120.551059
## iter 190 value 110.513525
## iter 200 value 104.982509
## iter 210 value 101.253721
## iter 220 value 98.973048
## iter 230 value 96.514495
## iter 240 value 93.610983
## iter 250 value 92.037124
## iter 260 value 90.675025
## iter 270 value 89.387361
## iter 280 value 87.703892
## iter 290 value 86.005334
## iter 300 value 84.452994
## iter 310 value 82.973975
## iter 320 value 81.251950
## iter 330 value 79.557965
## iter 340 value 77.728472
## iter 350 value 75.580478
## iter 360 value 73.771499
## iter 370 value 72.258401
## iter 380 value 70.581541
## iter 390 value 68.984386
## iter 400 value 67.475339
## iter 410 value 66.248227
## iter 420 value 64.570588
## iter 430 value 63.618426
## iter 440 value 62.963411
## iter 450 value 62.086356
## iter 460 value 61.403943
## iter 470 value 60.815296
## iter 480 value 60.207031
## iter 490 value 59.977514
## iter 500 value 59.866257
## final  value 59.866257 
## stopped after 500 iterations
## # weights:  25
## initial  value 1377807.125975 
## iter  10 value 6571.488159
## iter  20 value 5474.299869
## iter  30 value 5054.000702
## iter  40 value 4766.021984
## iter  50 value 4441.514471
## iter  60 value 3812.233641
## iter  70 value 1682.001345
## iter  80 value 1352.523134
## iter  90 value 1265.068886
## iter 100 value 1260.123184
## iter 110 value 1239.757906
## iter 120 value 1190.511134
## iter 130 value 1169.493519
## iter 140 value 1166.791414
## iter 150 value 1166.115966
## iter 160 value 1163.250475
## iter 170 value 1112.908266
## iter 180 value 1052.066823
## iter 190 value 959.498259
## iter 200 value 938.049932
## iter 210 value 931.818178
## iter 220 value 929.767684
## iter 230 value 929.584448
## iter 240 value 927.740950
## iter 250 value 926.080303
## iter 260 value 924.047051
## iter 270 value 923.330813
## iter 280 value 923.210748
## iter 290 value 922.871932
## iter 300 value 921.748841
## iter 310 value 921.248068
## iter 320 value 921.034681
## iter 330 value 921.031393
## iter 340 value 921.011617
## iter 350 value 920.986458
## iter 360 value 920.932646
## final  value 920.931819 
## converged
## # weights:  61
## initial  value 1394416.279146 
## iter  10 value 17208.579417
## iter  20 value 11420.971312
## iter  30 value 8894.743667
## iter  40 value 3894.354700
## iter  50 value 2457.172741
## iter  60 value 1560.736118
## iter  70 value 1432.108140
## iter  80 value 1327.600256
## iter  90 value 1297.485338
## iter 100 value 1272.102930
## iter 110 value 1206.245478
## iter 120 value 1192.445112
## iter 130 value 1120.609875
## iter 140 value 1111.261865
## iter 150 value 1067.033066
## iter 160 value 1000.138062
## iter 170 value 983.643036
## iter 180 value 967.826068
## iter 190 value 957.462638
## iter 200 value 944.098703
## iter 210 value 931.826221
## iter 220 value 926.470247
## iter 230 value 925.423011
## iter 240 value 925.330823
## iter 250 value 921.795980
## iter 260 value 918.060620
## iter 270 value 910.917522
## iter 280 value 875.982628
## iter 290 value 864.935981
## iter 300 value 855.815601
## iter 310 value 847.488670
## iter 320 value 829.575724
## iter 330 value 809.236075
## iter 340 value 798.556319
## iter 350 value 793.713913
## iter 360 value 784.493779
## iter 370 value 755.090620
## iter 380 value 738.768033
## iter 390 value 722.972468
## iter 400 value 710.575390
## iter 410 value 702.027676
## iter 420 value 698.319174
## iter 430 value 698.197091
## iter 440 value 697.300272
## iter 450 value 696.359848
## iter 460 value 694.890755
## iter 470 value 694.256092
## iter 480 value 694.234828
## iter 490 value 693.352075
## iter 500 value 681.434804
## final  value 681.434804 
## stopped after 500 iterations
## # weights:  121
## initial  value 1390362.490798 
## iter  10 value 2648.004641
## iter  20 value 1205.686882
## iter  30 value 917.755030
## iter  40 value 800.922374
## iter  50 value 692.218904
## iter  60 value 624.749929
## iter  70 value 588.463821
## iter  80 value 559.316647
## iter  90 value 532.620604
## iter 100 value 481.864839
## iter 110 value 441.655282
## iter 120 value 409.950081
## iter 130 value 389.881490
## iter 140 value 374.135902
## iter 150 value 361.803094
## iter 160 value 343.566365
## iter 170 value 328.360974
## iter 180 value 316.414340
## iter 190 value 310.646083
## iter 200 value 302.806307
## iter 210 value 295.855510
## iter 220 value 291.515606
## iter 230 value 287.724545
## iter 240 value 285.039908
## iter 250 value 282.966321
## iter 260 value 282.345764
## iter 270 value 281.698190
## iter 280 value 280.775431
## iter 290 value 279.881187
## iter 300 value 278.536968
## iter 310 value 275.788688
## iter 320 value 272.457381
## iter 330 value 269.747254
## iter 340 value 266.368074
## iter 350 value 264.436657
## iter 360 value 263.384848
## iter 370 value 262.949770
## iter 380 value 262.069030
## iter 390 value 261.497070
## iter 400 value 261.161976
## iter 410 value 260.863557
## iter 420 value 260.627309
## iter 430 value 260.478044
## iter 440 value 260.426514
## iter 450 value 260.411949
## iter 460 value 260.286238
## iter 470 value 259.842159
## iter 480 value 259.215802
## iter 490 value 258.932330
## iter 500 value 258.863038
## final  value 258.863038 
## stopped after 500 iterations
## # weights:  181
## initial  value 1383869.645027 
## iter  10 value 1091.540184
## iter  20 value 806.391746
## iter  30 value 684.300192
## iter  40 value 550.975552
## iter  50 value 462.011598
## iter  60 value 409.366577
## iter  70 value 378.826973
## iter  80 value 333.905667
## iter  90 value 305.722793
## iter 100 value 282.804996
## iter 110 value 263.892580
## iter 120 value 253.456544
## iter 130 value 239.647238
## iter 140 value 226.497078
## iter 150 value 205.807920
## iter 160 value 195.192175
## iter 170 value 186.773190
## iter 180 value 179.471846
## iter 190 value 171.393317
## iter 200 value 164.074367
## iter 210 value 158.300981
## iter 220 value 154.094084
## iter 230 value 150.373402
## iter 240 value 147.369726
## iter 250 value 144.867283
## iter 260 value 142.460081
## iter 270 value 140.767323
## iter 280 value 138.857173
## iter 290 value 137.717805
## iter 300 value 136.309401
## iter 310 value 134.101809
## iter 320 value 131.361328
## iter 330 value 127.308282
## iter 340 value 123.785654
## iter 350 value 121.889568
## iter 360 value 120.962597
## iter 370 value 120.642618
## iter 380 value 120.499033
## iter 390 value 120.330784
## iter 400 value 120.014842
## iter 410 value 119.594534
## iter 420 value 119.046423
## iter 430 value 118.216659
## iter 440 value 117.351317
## iter 450 value 116.225069
## iter 460 value 113.811917
## iter 470 value 110.930996
## iter 480 value 108.557149
## iter 490 value 106.887538
## iter 500 value 104.118412
## final  value 104.118412 
## stopped after 500 iterations
## # weights:  241
## initial  value 1291557.121004 
## iter  10 value 1821.909269
## iter  20 value 861.958014
## iter  30 value 662.823465
## iter  40 value 544.939462
## iter  50 value 437.716808
## iter  60 value 314.595553
## iter  70 value 273.299689
## iter  80 value 241.417904
## iter  90 value 212.191286
## iter 100 value 191.243296
## iter 110 value 172.740129
## iter 120 value 156.368282
## iter 130 value 143.913901
## iter 140 value 136.598269
## iter 150 value 130.852371
## iter 160 value 126.962558
## iter 170 value 122.262724
## iter 180 value 116.136330
## iter 190 value 108.954618
## iter 200 value 102.602115
## iter 210 value 96.899967
## iter 220 value 90.876366
## iter 230 value 85.493934
## iter 240 value 81.090770
## iter 250 value 75.938551
## iter 260 value 71.047903
## iter 270 value 67.060618
## iter 280 value 64.242047
## iter 290 value 62.755045
## iter 300 value 60.787180
## iter 310 value 59.112476
## iter 320 value 57.037132
## iter 330 value 55.713575
## iter 340 value 54.571772
## iter 350 value 53.424373
## iter 360 value 52.135964
## iter 370 value 51.249510
## iter 380 value 50.371189
## iter 390 value 49.141751
## iter 400 value 47.525856
## iter 410 value 46.521561
## iter 420 value 45.506862
## iter 430 value 44.740670
## iter 440 value 44.097112
## iter 450 value 43.625907
## iter 460 value 43.214077
## iter 470 value 42.941828
## iter 480 value 42.697766
## iter 490 value 42.587830
## iter 500 value 42.555918
## final  value 42.555918 
## stopped after 500 iterations
## # weights:  25
## initial  value 1372579.696316 
## iter  10 value 6620.517409
## iter  20 value 5371.553625
## iter  30 value 4710.588378
## iter  40 value 3381.651345
## iter  50 value 2038.840206
## iter  60 value 1502.700589
## iter  70 value 1398.475042
## iter  80 value 1381.993899
## iter  90 value 1377.585427
## iter 100 value 1343.262786
## iter 110 value 1331.907187
## iter 120 value 1327.214622
## iter 130 value 1324.885694
## iter 140 value 1324.447117
## iter 150 value 1323.962569
## iter 160 value 1322.367410
## iter 170 value 1321.739756
## iter 180 value 1321.181113
## iter 190 value 1321.015005
## iter 200 value 1320.965339
## iter 200 value 1320.965326
## iter 210 value 1320.951379
## final  value 1320.950668 
## converged
## # weights:  61
## initial  value 1409265.016599 
## iter  10 value 10053.213138
## iter  20 value 7517.004853
## iter  30 value 5843.041449
## iter  40 value 4020.598466
## iter  50 value 2771.229031
## iter  60 value 2043.457888
## iter  70 value 1480.405356
## iter  80 value 1243.951762
## iter  90 value 1039.848519
## iter 100 value 961.668231
## iter 110 value 916.073889
## iter 120 value 874.119902
## iter 130 value 860.379916
## iter 140 value 857.586689
## iter 150 value 840.522382
## iter 160 value 821.732941
## iter 170 value 812.938170
## iter 180 value 801.320859
## iter 190 value 795.407870
## iter 200 value 789.957027
## iter 210 value 786.922462
## iter 220 value 785.041856
## iter 230 value 782.237408
## iter 240 value 779.379164
## iter 250 value 778.291763
## iter 260 value 778.185551
## iter 270 value 777.770082
## iter 280 value 776.082089
## iter 290 value 773.983796
## iter 300 value 772.877835
## iter 310 value 772.390584
## iter 320 value 772.088415
## iter 330 value 771.796312
## iter 340 value 771.644337
## iter 350 value 771.475921
## iter 360 value 771.091682
## iter 370 value 768.154413
## iter 380 value 767.452754
## iter 390 value 766.548742
## iter 400 value 764.436492
## iter 410 value 759.922245
## iter 420 value 758.867103
## iter 430 value 758.382424
## iter 440 value 757.624797
## iter 450 value 757.410811
## iter 460 value 757.241282
## iter 470 value 757.171615
## iter 480 value 756.993092
## iter 490 value 756.651914
## iter 500 value 756.430855
## final  value 756.430855 
## stopped after 500 iterations
## # weights:  121
## initial  value 1348771.231863 
## iter  10 value 2153.978531
## iter  20 value 912.344946
## iter  30 value 757.075887
## iter  40 value 686.727619
## iter  50 value 624.326611
## iter  60 value 565.482013
## iter  70 value 516.615567
## iter  80 value 484.318447
## iter  90 value 460.023250
## iter 100 value 435.507731
## iter 110 value 413.719689
## iter 120 value 403.038519
## iter 130 value 392.183088
## iter 140 value 382.396634
## iter 150 value 378.081087
## iter 160 value 373.071054
## iter 170 value 366.675654
## iter 180 value 354.408421
## iter 190 value 341.185902
## iter 200 value 330.546475
## iter 210 value 322.778823
## iter 220 value 315.541504
## iter 230 value 311.442633
## iter 240 value 307.493502
## iter 250 value 306.600469
## iter 260 value 304.655438
## iter 270 value 301.876211
## iter 280 value 300.319028
## iter 290 value 299.356555
## iter 300 value 295.951787
## iter 310 value 293.427358
## iter 320 value 288.773531
## iter 330 value 278.850399
## iter 340 value 271.921014
## iter 350 value 266.329846
## iter 360 value 264.525485
## iter 370 value 263.862706
## iter 380 value 263.527306
## iter 390 value 263.305211
## iter 400 value 263.229136
## iter 410 value 263.126930
## iter 420 value 262.700327
## iter 430 value 259.663682
## iter 440 value 257.593203
## iter 450 value 256.158937
## iter 460 value 255.067427
## iter 470 value 254.534677
## iter 480 value 254.144963
## iter 490 value 254.047787
## iter 500 value 254.016384
## final  value 254.016384 
## stopped after 500 iterations
## # weights:  181
## initial  value 1430869.219900 
## iter  10 value 1203.169298
## iter  20 value 859.977069
## iter  30 value 702.674053
## iter  40 value 569.964810
## iter  50 value 442.915014
## iter  60 value 394.988684
## iter  70 value 359.139436
## iter  80 value 307.774216
## iter  90 value 278.912817
## iter 100 value 260.941975
## iter 110 value 242.976068
## iter 120 value 224.912520
## iter 130 value 210.697153
## iter 140 value 196.916289
## iter 150 value 184.704041
## iter 160 value 173.604627
## iter 170 value 166.666951
## iter 180 value 160.416798
## iter 190 value 154.357954
## iter 200 value 150.120943
## iter 210 value 144.965282
## iter 220 value 141.362726
## iter 230 value 135.811102
## iter 240 value 130.980937
## iter 250 value 128.243357
## iter 260 value 125.070794
## iter 270 value 122.509514
## iter 280 value 119.135124
## iter 290 value 112.570023
## iter 300 value 110.027261
## iter 310 value 107.064712
## iter 320 value 103.900749
## iter 330 value 102.107135
## iter 340 value 100.732817
## iter 350 value 99.894750
## iter 360 value 98.761885
## iter 370 value 98.528319
## iter 380 value 98.425918
## iter 390 value 98.223349
## iter 400 value 98.038588
## iter 410 value 97.607331
## iter 420 value 97.101352
## iter 430 value 96.323794
## iter 440 value 95.605735
## iter 450 value 95.083086
## iter 460 value 94.254628
## iter 470 value 93.481267
## iter 480 value 93.065227
## iter 490 value 92.681216
## iter 500 value 92.223155
## final  value 92.223155 
## stopped after 500 iterations
## # weights:  241
## initial  value 1447577.885567 
## iter  10 value 2744.914996
## iter  20 value 1146.500928
## iter  30 value 710.132165
## iter  40 value 535.028915
## iter  50 value 457.015547
## iter  60 value 364.493274
## iter  70 value 295.639634
## iter  80 value 246.729285
## iter  90 value 218.204345
## iter 100 value 200.386817
## iter 110 value 187.434772
## iter 120 value 179.928689
## iter 130 value 172.975121
## iter 140 value 168.659944
## iter 150 value 164.002296
## iter 160 value 160.826762
## iter 170 value 157.833650
## iter 180 value 153.927268
## iter 190 value 148.447355
## iter 200 value 141.176566
## iter 210 value 134.207882
## iter 220 value 128.623969
## iter 230 value 122.603852
## iter 240 value 117.775713
## iter 250 value 114.418955
## iter 260 value 111.123711
## iter 270 value 108.801299
## iter 280 value 106.559617
## iter 290 value 104.848651
## iter 300 value 103.161185
## iter 310 value 101.336099
## iter 320 value 99.485886
## iter 330 value 97.426213
## iter 340 value 95.503081
## iter 350 value 93.851554
## iter 360 value 92.228853
## iter 370 value 90.614183
## iter 380 value 88.174462
## iter 390 value 85.935415
## iter 400 value 84.092588
## iter 410 value 82.507959
## iter 420 value 80.844932
## iter 430 value 79.050224
## iter 440 value 77.653474
## iter 450 value 76.659325
## iter 460 value 75.789712
## iter 470 value 75.197178
## iter 480 value 74.450062
## iter 490 value 74.161486
## iter 500 value 74.124362
## final  value 74.124362 
## stopped after 500 iterations
## # weights:  25
## initial  value 1399249.548941 
## iter  10 value 7003.306263
## iter  20 value 5131.185983
## iter  30 value 2857.338188
## iter  40 value 1762.670837
## iter  50 value 1286.671748
## iter  60 value 1246.243551
## iter  70 value 1225.658074
## iter  80 value 1158.227390
## iter  90 value 1143.215915
## iter 100 value 1135.833051
## iter 110 value 1134.240530
## iter 120 value 1133.685525
## iter 130 value 1132.255449
## iter 140 value 1130.797270
## iter 150 value 1129.767567
## iter 160 value 1129.447015
## iter 170 value 1129.435259
## iter 180 value 1129.239655
## iter 190 value 1128.799157
## iter 200 value 1128.619499
## final  value 1128.583892 
## converged
## # weights:  61
## initial  value 1370760.779359 
## iter  10 value 19907.146053
## iter  20 value 3239.762629
## iter  30 value 3102.842849
## iter  40 value 3013.132086
## iter  50 value 2948.650387
## iter  60 value 2893.249232
## iter  70 value 2819.007379
## iter  80 value 2782.237329
## iter  90 value 2762.406226
## iter 100 value 2758.731294
## iter 110 value 2755.521585
## iter 120 value 2751.978833
## iter 130 value 2735.855152
## iter 140 value 2733.920928
## iter 150 value 2733.262579
## iter 160 value 2732.679618
## iter 170 value 2731.842361
## iter 180 value 2731.573955
## iter 190 value 2729.314204
## iter 200 value 2713.701059
## iter 210 value 2706.317183
## iter 220 value 2691.657400
## iter 230 value 2689.880493
## iter 240 value 2678.279249
## iter 250 value 2667.300336
## iter 260 value 2666.123031
## iter 270 value 2640.826370
## iter 280 value 2638.156135
## iter 290 value 2628.133612
## iter 300 value 2403.963658
## iter 310 value 2044.160707
## iter 320 value 1475.744751
## iter 330 value 1272.201363
## iter 340 value 1206.136339
## iter 350 value 1194.682270
## iter 360 value 1190.650892
## iter 370 value 1189.620567
## iter 380 value 1189.118859
## iter 390 value 1187.988302
## iter 400 value 1186.085736
## iter 410 value 1186.062944
## final  value 1186.062177 
## converged
## # weights:  121
## initial  value 1351488.804482 
## iter  10 value 1274.506092
## iter  20 value 900.715802
## iter  30 value 757.656657
## iter  40 value 644.489324
## iter  50 value 564.932621
## iter  60 value 520.276777
## iter  70 value 471.698120
## iter  80 value 442.779524
## iter  90 value 422.462933
## iter 100 value 408.357824
## iter 110 value 395.506349
## iter 120 value 384.665575
## iter 130 value 376.388211
## iter 140 value 368.343874
## iter 150 value 361.125107
## iter 160 value 354.937938
## iter 170 value 350.923256
## iter 180 value 344.337857
## iter 190 value 337.660117
## iter 200 value 333.167933
## iter 210 value 330.114755
## iter 220 value 327.808591
## iter 230 value 324.523861
## iter 240 value 322.006802
## iter 250 value 320.853066
## iter 260 value 320.224749
## iter 270 value 317.176238
## iter 280 value 313.440666
## iter 290 value 310.918030
## iter 300 value 309.519708
## iter 310 value 307.794084
## iter 320 value 303.841030
## iter 330 value 300.399886
## iter 340 value 299.564688
## iter 350 value 298.166221
## iter 360 value 297.639823
## iter 370 value 297.337774
## iter 380 value 295.457185
## iter 390 value 293.231000
## iter 400 value 292.067611
## iter 410 value 290.788416
## iter 420 value 287.644827
## iter 430 value 285.054687
## iter 440 value 283.389651
## iter 450 value 281.085013
## iter 460 value 278.140069
## iter 470 value 276.125443
## iter 480 value 274.818999
## iter 490 value 273.786823
## iter 500 value 273.615208
## final  value 273.615208 
## stopped after 500 iterations
## # weights:  181
## initial  value 1415817.314266 
## iter  10 value 1153.746106
## iter  20 value 832.706463
## iter  30 value 736.038801
## iter  40 value 615.890780
## iter  50 value 494.425847
## iter  60 value 430.101505
## iter  70 value 376.750882
## iter  80 value 328.624886
## iter  90 value 303.685305
## iter 100 value 278.555990
## iter 110 value 251.284476
## iter 120 value 237.195157
## iter 130 value 224.938165
## iter 140 value 215.014507
## iter 150 value 208.263521
## iter 160 value 202.777010
## iter 170 value 198.386358
## iter 180 value 193.232424
## iter 190 value 189.899769
## iter 200 value 186.888340
## iter 210 value 182.727949
## iter 220 value 180.164476
## iter 230 value 176.324788
## iter 240 value 174.429170
## iter 250 value 173.034823
## iter 260 value 172.218962
## iter 270 value 171.557560
## iter 280 value 171.038350
## iter 290 value 170.584040
## iter 300 value 170.214840
## iter 310 value 169.397091
## iter 320 value 168.188245
## iter 330 value 166.969432
## iter 340 value 165.835678
## iter 350 value 164.841096
## iter 360 value 163.915017
## iter 370 value 163.153419
## iter 380 value 162.791262
## iter 390 value 162.174939
## iter 400 value 161.237640
## iter 410 value 160.027661
## iter 420 value 158.675966
## iter 430 value 157.343618
## iter 440 value 155.833902
## iter 450 value 154.797980
## iter 460 value 153.169628
## iter 470 value 150.788225
## iter 480 value 148.332568
## iter 490 value 145.043753
## iter 500 value 142.875852
## final  value 142.875852 
## stopped after 500 iterations
## # weights:  241
## initial  value 1369819.313009 
## iter  10 value 1539.309919
## iter  20 value 838.402909
## iter  30 value 728.270365
## iter  40 value 562.696980
## iter  50 value 438.775254
## iter  60 value 372.495928
## iter  70 value 320.728187
## iter  80 value 289.415911
## iter  90 value 248.421982
## iter 100 value 220.815497
## iter 110 value 204.095880
## iter 120 value 186.211479
## iter 130 value 171.728556
## iter 140 value 160.688435
## iter 150 value 153.035179
## iter 160 value 146.617843
## iter 170 value 138.845792
## iter 180 value 133.165401
## iter 190 value 129.035622
## iter 200 value 124.474376
## iter 210 value 119.018202
## iter 220 value 112.296760
## iter 230 value 107.162626
## iter 240 value 103.136083
## iter 250 value 97.478991
## iter 260 value 92.199305
## iter 270 value 88.770772
## iter 280 value 87.430672
## iter 290 value 86.579492
## iter 300 value 86.023614
## iter 310 value 84.610445
## iter 320 value 83.296348
## iter 330 value 82.022265
## iter 340 value 80.599078
## iter 350 value 79.400415
## iter 360 value 78.445209
## iter 370 value 77.183573
## iter 380 value 75.491217
## iter 390 value 72.918922
## iter 400 value 70.337756
## iter 410 value 68.710865
## iter 420 value 66.708595
## iter 430 value 64.351618
## iter 440 value 60.124377
## iter 450 value 55.873978
## iter 460 value 53.803475
## iter 470 value 52.027364
## iter 480 value 50.823363
## iter 490 value 50.311411
## iter 500 value 50.163579
## final  value 50.163579 
## stopped after 500 iterations
## # weights:  25
## initial  value 1400994.033681 
## iter  10 value 7955.493644
## iter  20 value 4601.359282
## iter  30 value 3276.361673
## iter  40 value 2016.038266
## iter  50 value 1769.808288
## iter  60 value 1634.133437
## iter  70 value 1395.903713
## iter  80 value 1277.046361
## iter  90 value 1237.554775
## iter 100 value 1220.745836
## iter 110 value 1207.940911
## iter 120 value 1192.950222
## iter 130 value 1190.361158
## final  value 1190.338875 
## converged
## # weights:  61
## initial  value 1421368.223731 
## iter  10 value 4612.660994
## iter  20 value 2656.081762
## iter  30 value 2230.571394
## iter  40 value 1952.037648
## iter  50 value 1604.357762
## iter  60 value 1281.314455
## iter  70 value 1155.182669
## iter  80 value 1085.957883
## iter  90 value 1027.889277
## iter 100 value 975.513376
## iter 110 value 945.952700
## iter 120 value 934.648888
## iter 130 value 928.719033
## iter 140 value 924.923107
## iter 150 value 924.209844
## iter 160 value 922.794149
## iter 170 value 919.344881
## iter 180 value 912.969396
## iter 190 value 909.098227
## iter 200 value 904.750679
## iter 210 value 889.186522
## iter 220 value 880.361651
## iter 230 value 878.443738
## iter 240 value 877.397079
## iter 250 value 876.936345
## iter 260 value 874.059015
## iter 270 value 872.065119
## iter 280 value 870.797129
## iter 290 value 868.427374
## iter 300 value 862.199592
## iter 310 value 847.690135
## iter 320 value 839.174099
## iter 330 value 832.954565
## iter 340 value 830.164821
## iter 350 value 829.945493
## iter 360 value 829.915428
## iter 370 value 829.908993
## iter 370 value 829.908988
## iter 370 value 829.908988
## final  value 829.908988 
## converged
## # weights:  121
## initial  value 1415604.581887 
## iter  10 value 1227.769186
## iter  20 value 953.745401
## iter  30 value 806.372137
## iter  40 value 732.668901
## iter  50 value 682.436079
## iter  60 value 646.732870
## iter  70 value 630.909842
## iter  80 value 617.691505
## iter  90 value 599.218991
## iter 100 value 583.782858
## iter 110 value 568.961902
## iter 120 value 557.690120
## iter 130 value 549.099975
## iter 140 value 543.626355
## iter 150 value 537.202356
## iter 160 value 533.899409
## iter 170 value 526.474566
## iter 180 value 522.856691
## iter 190 value 519.471537
## iter 200 value 514.946737
## iter 210 value 511.605796
## iter 220 value 506.280424
## iter 230 value 503.509765
## iter 240 value 499.352679
## iter 250 value 496.877813
## iter 260 value 495.376233
## iter 270 value 493.164283
## iter 280 value 492.013969
## iter 290 value 490.904499
## iter 300 value 490.447125
## iter 310 value 490.142662
## iter 320 value 489.946515
## iter 330 value 489.869201
## iter 340 value 489.851547
## iter 350 value 489.849968
## final  value 489.849843 
## converged
## # weights:  181
## initial  value 1355874.647659 
## iter  10 value 1268.762649
## iter  20 value 891.091397
## iter  30 value 786.235972
## iter  40 value 681.113258
## iter  50 value 589.561179
## iter  60 value 551.597653
## iter  70 value 524.484523
## iter  80 value 501.372322
## iter  90 value 486.972949
## iter 100 value 476.462205
## iter 110 value 466.885857
## iter 120 value 457.812185
## iter 130 value 449.434337
## iter 140 value 437.278834
## iter 150 value 427.452600
## iter 160 value 417.590269
## iter 170 value 410.122513
## iter 180 value 403.411050
## iter 190 value 398.432436
## iter 200 value 392.773472
## iter 210 value 389.850201
## iter 220 value 387.486318
## iter 230 value 386.011127
## iter 240 value 384.624139
## iter 250 value 381.979273
## iter 260 value 379.263554
## iter 270 value 377.934958
## iter 280 value 376.958222
## iter 290 value 376.446093
## iter 300 value 376.054584
## iter 310 value 375.569120
## iter 320 value 375.023193
## iter 330 value 374.599039
## iter 340 value 374.019086
## iter 350 value 373.631419
## iter 360 value 373.334406
## iter 370 value 373.200845
## iter 380 value 373.078076
## iter 390 value 372.948732
## iter 400 value 372.846201
## iter 410 value 372.814350
## iter 420 value 372.794035
## iter 430 value 372.783049
## iter 440 value 372.780478
## iter 450 value 372.779891
## final  value 372.779828 
## converged
## # weights:  241
## initial  value 1372262.491724 
## iter  10 value 1261.420175
## iter  20 value 903.327745
## iter  30 value 786.358543
## iter  40 value 659.204554
## iter  50 value 578.880153
## iter  60 value 539.695219
## iter  70 value 510.418071
## iter  80 value 485.319447
## iter  90 value 462.890066
## iter 100 value 443.788352
## iter 110 value 435.121937
## iter 120 value 429.100086
## iter 130 value 421.543425
## iter 140 value 416.127981
## iter 150 value 411.128164
## iter 160 value 405.615261
## iter 170 value 400.909078
## iter 180 value 397.308013
## iter 190 value 395.236362
## iter 200 value 393.160042
## iter 210 value 390.764108
## iter 220 value 387.820626
## iter 230 value 382.183228
## iter 240 value 371.001090
## iter 250 value 362.754123
## iter 260 value 356.765498
## iter 270 value 351.158632
## iter 280 value 346.726676
## iter 290 value 343.983664
## iter 300 value 341.073193
## iter 310 value 338.323878
## iter 320 value 335.814223
## iter 330 value 333.789370
## iter 340 value 332.110082
## iter 350 value 330.837736
## iter 360 value 329.500651
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## iter 380 value 328.123725
## iter 390 value 327.284490
## iter 400 value 326.824997
## iter 410 value 326.579386
## iter 420 value 326.424029
## iter 430 value 326.262638
## iter 440 value 325.792207
## iter 450 value 324.905723
## iter 460 value 324.172357
## iter 470 value 323.327584
## iter 480 value 322.741780
## iter 490 value 322.509284
## iter 500 value 322.257719
## final  value 322.257719 
## stopped after 500 iterations
## # weights:  25
## initial  value 1369324.548606 
## iter  10 value 7029.271043
## iter  20 value 5520.054754
## iter  30 value 3642.486405
## iter  40 value 2619.572918
## iter  50 value 1567.967587
## iter  60 value 1480.984936
## iter  70 value 1461.370256
## iter  80 value 1451.908237
## iter  90 value 1426.602109
## iter 100 value 1417.057151
## iter 110 value 1411.328755
## iter 120 value 1410.344311
## iter 130 value 1409.957226
## iter 140 value 1403.711795
## iter 150 value 1370.224330
## iter 160 value 1355.550457
## iter 170 value 1349.305219
## iter 180 value 1306.933094
## iter 190 value 1288.582534
## iter 200 value 1163.049697
## iter 210 value 992.138398
## iter 220 value 935.697053
## iter 230 value 928.871012
## iter 240 value 928.707657
## iter 250 value 923.356768
## iter 260 value 917.870399
## iter 270 value 916.347507
## iter 280 value 914.823268
## iter 290 value 914.821357
## iter 300 value 914.813899
## iter 310 value 914.791238
## final  value 914.790295 
## converged
## # weights:  61
## initial  value 1403025.240253 
## iter  10 value 4728.121653
## iter  20 value 2673.376292
## iter  30 value 1989.686211
## iter  40 value 1598.316078
## iter  50 value 1142.365653
## iter  60 value 938.230331
## iter  70 value 823.159675
## iter  80 value 782.372632
## iter  90 value 751.734381
## iter 100 value 723.270320
## iter 110 value 709.153813
## iter 120 value 690.189915
## iter 130 value 682.817015
## iter 140 value 681.126544
## iter 150 value 676.342571
## iter 160 value 664.105838
## iter 170 value 657.703261
## iter 180 value 653.514365
## iter 190 value 651.402453
## iter 200 value 647.976011
## iter 210 value 646.449817
## iter 220 value 645.405267
## iter 230 value 643.520719
## iter 240 value 641.420334
## iter 250 value 639.915630
## iter 260 value 639.633704
## iter 270 value 639.039992
## iter 280 value 637.250225
## iter 290 value 634.685070
## iter 300 value 629.834822
## iter 310 value 628.067869
## iter 320 value 625.918633
## iter 330 value 620.157621
## iter 340 value 614.206570
## iter 350 value 602.877392
## iter 360 value 590.663175
## iter 370 value 584.956312
## iter 380 value 584.585613
## iter 390 value 584.085472
## iter 400 value 583.271979
## iter 410 value 582.492346
## iter 420 value 581.155864
## iter 430 value 580.221530
## iter 440 value 579.555623
## iter 450 value 579.196360
## iter 460 value 578.968396
## iter 470 value 578.781263
## iter 480 value 578.671547
## iter 490 value 578.628224
## iter 500 value 578.617326
## final  value 578.617326 
## stopped after 500 iterations
## # weights:  121
## initial  value 1320129.751655 
## iter  10 value 1625.228776
## iter  20 value 915.604214
## iter  30 value 790.285464
## iter  40 value 683.936799
## iter  50 value 620.961902
## iter  60 value 562.910648
## iter  70 value 521.430401
## iter  80 value 478.618614
## iter  90 value 439.816602
## iter 100 value 419.157181
## iter 110 value 401.197026
## iter 120 value 381.863357
## iter 130 value 363.914179
## iter 140 value 351.182524
## iter 150 value 345.134857
## iter 160 value 338.062706
## iter 170 value 334.163435
## iter 180 value 331.160302
## iter 190 value 328.187079
## iter 200 value 324.922017
## iter 210 value 322.617829
## iter 220 value 321.087773
## iter 230 value 320.451622
## iter 240 value 318.674227
## iter 250 value 314.127727
## iter 260 value 308.242430
## iter 270 value 302.461818
## iter 280 value 298.020699
## iter 290 value 291.990313
## iter 300 value 287.168533
## iter 310 value 284.720694
## iter 320 value 282.705927
## iter 330 value 280.681581
## iter 340 value 275.189209
## iter 350 value 273.266606
## iter 360 value 272.846296
## iter 370 value 272.711421
## iter 380 value 272.588512
## iter 390 value 272.383714
## iter 400 value 271.220670
## iter 410 value 270.770136
## iter 420 value 270.655640
## iter 430 value 270.638546
## iter 440 value 270.634910
## iter 450 value 270.630764
## iter 460 value 270.621230
## iter 470 value 270.557530
## iter 480 value 270.485869
## iter 490 value 270.327169
## iter 500 value 270.219184
## final  value 270.219184 
## stopped after 500 iterations
## # weights:  181
## initial  value 1397857.787408 
## iter  10 value 1390.762477
## iter  20 value 826.968495
## iter  30 value 667.515999
## iter  40 value 541.885557
## iter  50 value 439.611594
## iter  60 value 396.945403
## iter  70 value 358.394733
## iter  80 value 297.710310
## iter  90 value 271.522436
## iter 100 value 253.055187
## iter 110 value 239.202558
## iter 120 value 229.915185
## iter 130 value 224.379846
## iter 140 value 219.063936
## iter 150 value 212.612215
## iter 160 value 205.550331
## iter 170 value 196.820685
## iter 180 value 192.324688
## iter 190 value 188.121140
## iter 200 value 184.528932
## iter 210 value 181.329476
## iter 220 value 179.018566
## iter 230 value 177.630247
## iter 240 value 176.020754
## iter 250 value 175.047421
## iter 260 value 173.448251
## iter 270 value 171.180713
## iter 280 value 169.821153
## iter 290 value 168.647747
## iter 300 value 167.921012
## iter 310 value 166.723165
## iter 320 value 165.211617
## iter 330 value 164.230314
## iter 340 value 163.490978
## iter 350 value 163.113115
## iter 360 value 162.773775
## iter 370 value 162.594977
## iter 380 value 162.538072
## iter 390 value 162.448458
## iter 400 value 162.288123
## iter 410 value 162.151497
## iter 420 value 161.819931
## iter 430 value 161.346358
## iter 440 value 160.769040
## iter 450 value 160.349931
## iter 460 value 159.895578
## iter 470 value 159.418228
## iter 480 value 159.097541
## iter 490 value 158.612176
## iter 500 value 158.141032
## final  value 158.141032 
## stopped after 500 iterations
## # weights:  241
## initial  value 1359008.804843 
## iter  10 value 1463.672232
## iter  20 value 724.554487
## iter  30 value 567.537212
## iter  40 value 444.004569
## iter  50 value 359.217529
## iter  60 value 305.192641
## iter  70 value 257.194675
## iter  80 value 213.266795
## iter  90 value 176.734721
## iter 100 value 149.220085
## iter 110 value 127.363720
## iter 120 value 113.104715
## iter 130 value 100.390078
## iter 140 value 89.673576
## iter 150 value 84.463974
## iter 160 value 79.801238
## iter 170 value 75.102829
## iter 180 value 70.996846
## iter 190 value 67.144328
## iter 200 value 64.149670
## iter 210 value 61.229742
## iter 220 value 58.877169
## iter 230 value 57.399662
## iter 240 value 55.870285
## iter 250 value 54.161959
## iter 260 value 52.517565
## iter 270 value 50.894570
## iter 280 value 48.717930
## iter 290 value 46.394418
## iter 300 value 43.626692
## iter 310 value 42.066893
## iter 320 value 40.702205
## iter 330 value 39.940052
## iter 340 value 39.075710
## iter 350 value 38.292622
## iter 360 value 37.718801
## iter 370 value 37.356665
## iter 380 value 36.955996
## iter 390 value 36.549113
## iter 400 value 36.255393
## iter 410 value 35.946223
## iter 420 value 35.680790
## iter 430 value 35.394340
## iter 440 value 35.160865
## iter 450 value 34.341863
## iter 460 value 33.614744
## iter 470 value 33.416196
## iter 480 value 33.092091
## iter 490 value 32.927526
## iter 500 value 32.901611
## final  value 32.901611 
## stopped after 500 iterations
## # weights:  25
## initial  value 1417650.248740 
## iter  10 value 4079.225355
## iter  20 value 2720.183206
## iter  30 value 2153.001104
## iter  40 value 1266.636488
## iter  50 value 1009.920115
## iter  60 value 968.252913
## iter  70 value 963.521993
## iter  80 value 957.007153
## iter  90 value 933.375612
## iter 100 value 923.142123
## iter 110 value 918.738126
## iter 120 value 918.257227
## iter 130 value 918.190563
## iter 140 value 916.313358
## iter 150 value 915.017274
## iter 160 value 914.738751
## iter 170 value 914.597430
## iter 180 value 914.434302
## iter 190 value 914.075926
## iter 200 value 913.812450
## iter 210 value 913.105557
## iter 220 value 913.067708
## iter 230 value 913.049154
## iter 240 value 912.993866
## iter 250 value 912.961737
## iter 260 value 912.958338
## iter 270 value 912.944660
## iter 280 value 912.574581
## iter 290 value 912.414871
## iter 300 value 912.369872
## iter 310 value 912.368927
## iter 320 value 912.366370
## iter 330 value 912.335954
## iter 340 value 912.196131
## iter 350 value 912.186757
## final  value 912.186740 
## converged
## # weights:  61
## initial  value 1401538.991714 
## iter  10 value 3097.422086
## iter  20 value 1321.082665
## iter  30 value 1084.749567
## iter  40 value 928.812640
## iter  50 value 824.877921
## iter  60 value 754.348125
## iter  70 value 700.817491
## iter  80 value 669.033675
## iter  90 value 651.990679
## iter 100 value 644.846596
## iter 110 value 638.290014
## iter 120 value 634.548820
## iter 130 value 632.142067
## iter 140 value 631.320697
## iter 150 value 628.739928
## iter 160 value 622.614609
## iter 170 value 616.196509
## iter 180 value 610.632030
## iter 190 value 603.898426
## iter 200 value 599.312530
## iter 210 value 596.152643
## iter 220 value 594.972528
## iter 230 value 594.783262
## iter 240 value 594.636335
## iter 250 value 594.397304
## iter 260 value 594.360715
## iter 270 value 594.334446
## iter 280 value 594.291013
## iter 290 value 594.201726
## iter 300 value 593.742283
## iter 310 value 592.457381
## iter 320 value 592.233946
## iter 330 value 592.164805
## iter 340 value 591.251286
## iter 350 value 588.740027
## iter 360 value 586.385263
## iter 370 value 584.552889
## iter 380 value 583.957106
## iter 390 value 582.833310
## iter 400 value 582.066072
## iter 410 value 581.635652
## iter 420 value 581.299431
## iter 430 value 580.926102
## iter 440 value 580.843110
## iter 450 value 580.773764
## iter 460 value 580.597019
## iter 470 value 580.245898
## iter 480 value 579.709386
## iter 490 value 579.176393
## iter 500 value 579.061910
## final  value 579.061910 
## stopped after 500 iterations
## # weights:  121
## initial  value 1387618.117100 
## iter  10 value 2753.266913
## iter  20 value 1898.073403
## iter  30 value 1368.692358
## iter  40 value 1060.779871
## iter  50 value 867.633050
## iter  60 value 761.397845
## iter  70 value 663.727604
## iter  80 value 606.383719
## iter  90 value 581.162546
## iter 100 value 557.953089
## iter 110 value 519.416820
## iter 120 value 492.719696
## iter 130 value 477.416876
## iter 140 value 469.827639
## iter 150 value 464.750797
## iter 160 value 456.332048
## iter 170 value 450.593717
## iter 180 value 446.753656
## iter 190 value 445.162577
## iter 200 value 444.297708
## iter 210 value 442.805780
## iter 220 value 440.465591
## iter 230 value 433.331744
## iter 240 value 415.970457
## iter 250 value 403.170380
## iter 260 value 372.884777
## iter 270 value 357.041614
## iter 280 value 345.949303
## iter 290 value 341.416040
## iter 300 value 338.189701
## iter 310 value 336.936550
## iter 320 value 336.277514
## iter 330 value 335.729550
## iter 340 value 334.893200
## iter 350 value 332.942310
## iter 360 value 329.967863
## iter 370 value 328.713499
## iter 380 value 328.093460
## iter 390 value 327.822091
## iter 400 value 327.594812
## iter 410 value 327.074886
## iter 420 value 326.561507
## iter 430 value 325.950176
## iter 440 value 325.795840
## iter 450 value 325.722986
## iter 460 value 325.627336
## iter 470 value 325.532279
## iter 480 value 325.509558
## iter 490 value 325.486698
## iter 500 value 325.464283
## final  value 325.464283 
## stopped after 500 iterations
## # weights:  181
## initial  value 1353109.831716 
## iter  10 value 1164.641755
## iter  20 value 842.889898
## iter  30 value 654.110766
## iter  40 value 535.081291
## iter  50 value 457.116836
## iter  60 value 400.020065
## iter  70 value 347.795060
## iter  80 value 308.961724
## iter  90 value 284.603092
## iter 100 value 266.385862
## iter 110 value 248.130578
## iter 120 value 226.692360
## iter 130 value 214.315218
## iter 140 value 203.502994
## iter 150 value 196.588808
## iter 160 value 188.659609
## iter 170 value 178.661166
## iter 180 value 168.866160
## iter 190 value 162.763919
## iter 200 value 158.164312
## iter 210 value 154.602992
## iter 220 value 150.805383
## iter 230 value 147.222510
## iter 240 value 142.764118
## iter 250 value 139.603465
## iter 260 value 137.886382
## iter 270 value 137.184018
## iter 280 value 136.409233
## iter 290 value 135.787799
## iter 300 value 135.031219
## iter 310 value 134.245380
## iter 320 value 132.949438
## iter 330 value 131.470211
## iter 340 value 130.573451
## iter 350 value 129.976660
## iter 360 value 129.553653
## iter 370 value 129.398281
## iter 380 value 129.361870
## iter 390 value 129.234536
## iter 400 value 129.105458
## iter 410 value 128.880500
## iter 420 value 128.593748
## iter 430 value 127.987667
## iter 440 value 127.251844
## iter 450 value 126.518308
## iter 460 value 126.031364
## iter 470 value 125.729110
## iter 480 value 125.151554
## iter 490 value 124.254815
## iter 500 value 123.839458
## final  value 123.839458 
## stopped after 500 iterations
## # weights:  241
## initial  value 1405068.188133 
## iter  10 value 1595.043138
## iter  20 value 794.538815
## iter  30 value 622.075932
## iter  40 value 508.212107
## iter  50 value 434.460884
## iter  60 value 381.855581
## iter  70 value 340.121663
## iter  80 value 309.685292
## iter  90 value 282.875985
## iter 100 value 250.235515
## iter 110 value 226.028747
## iter 120 value 198.101790
## iter 130 value 176.233743
## iter 140 value 163.915536
## iter 150 value 149.035397
## iter 160 value 137.196510
## iter 170 value 127.560285
## iter 180 value 115.175385
## iter 190 value 108.027043
## iter 200 value 102.948023
## iter 210 value 98.710898
## iter 220 value 94.405929
## iter 230 value 90.147889
## iter 240 value 87.410015
## iter 250 value 83.590916
## iter 260 value 79.918609
## iter 270 value 77.590213
## iter 280 value 75.670494
## iter 290 value 73.986850
## iter 300 value 72.350914
## iter 310 value 71.147824
## iter 320 value 70.255227
## iter 330 value 69.022114
## iter 340 value 67.050224
## iter 350 value 65.485476
## iter 360 value 64.468371
## iter 370 value 63.751510
## iter 380 value 63.130973
## iter 390 value 62.306791
## iter 400 value 61.529457
## iter 410 value 61.073894
## iter 420 value 60.504387
## iter 430 value 59.688065
## iter 440 value 59.060862
## iter 450 value 58.333372
## iter 460 value 57.933664
## iter 470 value 57.739341
## iter 480 value 57.574134
## iter 490 value 57.512981
## iter 500 value 57.491683
## final  value 57.491683 
## stopped after 500 iterations
## # weights:  25
## initial  value 1400163.771020 
## iter  10 value 5713.801711
## iter  20 value 5428.728029
## iter  30 value 4766.190000
## iter  40 value 4279.220641
## iter  50 value 3418.831557
## iter  60 value 1868.851241
## iter  70 value 1138.388766
## iter  80 value 973.124748
## iter  90 value 958.627804
## iter 100 value 945.266446
## iter 110 value 935.742619
## iter 120 value 931.612337
## iter 130 value 930.479714
## iter 140 value 929.992672
## iter 150 value 928.878328
## iter 160 value 927.360784
## iter 170 value 926.832065
## iter 180 value 925.766405
## iter 190 value 925.756816
## iter 200 value 925.710542
## iter 210 value 925.253097
## iter 220 value 924.683910
## iter 230 value 924.342876
## iter 240 value 924.339863
## iter 250 value 924.329347
## iter 260 value 924.322652
## iter 270 value 924.185346
## iter 280 value 924.118729
## iter 290 value 924.116347
## iter 290 value 924.116345
## iter 300 value 924.116080
## iter 300 value 924.116079
## final  value 924.115967 
## converged
## # weights:  61
## initial  value 1385279.120146 
## iter  10 value 2381.166495
## iter  20 value 1422.384054
## iter  30 value 1194.124622
## iter  40 value 1024.783772
## iter  50 value 913.654068
## iter  60 value 834.222527
## iter  70 value 778.881039
## iter  80 value 717.896567
## iter  90 value 678.846658
## iter 100 value 661.933289
## iter 110 value 646.737066
## iter 120 value 640.994490
## iter 130 value 638.638933
## iter 140 value 637.807904
## iter 150 value 637.174873
## iter 160 value 636.854474
## iter 170 value 635.755854
## iter 180 value 634.507177
## iter 190 value 633.811073
## iter 200 value 631.931574
## iter 210 value 627.945953
## iter 220 value 622.966155
## iter 230 value 619.847091
## iter 240 value 618.189197
## iter 250 value 617.795579
## iter 260 value 617.536832
## iter 270 value 617.505557
## iter 280 value 617.448387
## iter 290 value 617.297451
## iter 300 value 617.062309
## iter 310 value 616.858225
## iter 320 value 616.746581
## iter 330 value 615.559596
## iter 340 value 614.895280
## iter 350 value 613.692400
## iter 360 value 613.231901
## iter 370 value 613.101282
## iter 380 value 613.032528
## iter 390 value 612.895499
## iter 400 value 612.781296
## iter 410 value 612.717414
## iter 420 value 612.524121
## iter 430 value 612.344326
## iter 440 value 612.329520
## iter 450 value 612.321211
## final  value 612.321145 
## converged
## # weights:  121
## initial  value 1417212.408072 
## iter  10 value 5280.796476
## iter  20 value 1688.081874
## iter  30 value 1234.127556
## iter  40 value 1007.940097
## iter  50 value 837.389238
## iter  60 value 754.129475
## iter  70 value 734.818821
## iter  80 value 724.310592
## iter  90 value 694.662279
## iter 100 value 664.287987
## iter 110 value 647.795237
## iter 120 value 628.397121
## iter 130 value 618.382490
## iter 140 value 601.778486
## iter 150 value 588.765063
## iter 160 value 579.641514
## iter 170 value 572.482270
## iter 180 value 566.658088
## iter 190 value 562.289212
## iter 200 value 558.693466
## iter 210 value 551.187800
## iter 220 value 543.161134
## iter 230 value 537.673573
## iter 240 value 533.990321
## iter 250 value 529.678997
## iter 260 value 527.575958
## iter 270 value 526.859297
## iter 280 value 526.778131
## iter 290 value 526.703185
## iter 300 value 526.602259
## iter 310 value 526.313449
## iter 320 value 526.145045
## iter 330 value 525.997876
## iter 340 value 525.811418
## iter 350 value 525.798071
## iter 360 value 525.791518
## iter 370 value 525.778542
## iter 380 value 525.651566
## iter 390 value 522.813974
## iter 400 value 517.316646
## iter 410 value 516.673200
## iter 420 value 516.326686
## iter 430 value 516.172212
## iter 440 value 516.101253
## iter 450 value 516.040796
## iter 460 value 515.868078
## iter 470 value 515.784470
## iter 480 value 514.908834
## iter 490 value 514.074078
## iter 500 value 513.029929
## final  value 513.029929 
## stopped after 500 iterations
## # weights:  181
## initial  value 1389328.202115 
## iter  10 value 1244.986765
## iter  20 value 841.651698
## iter  30 value 694.103831
## iter  40 value 539.807267
## iter  50 value 470.187909
## iter  60 value 437.291794
## iter  70 value 409.322621
## iter  80 value 359.162507
## iter  90 value 305.667595
## iter 100 value 272.434827
## iter 110 value 245.040035
## iter 120 value 226.838346
## iter 130 value 214.270839
## iter 140 value 202.473113
## iter 150 value 193.078213
## iter 160 value 187.944295
## iter 170 value 180.063615
## iter 180 value 172.096401
## iter 190 value 164.002801
## iter 200 value 152.796253
## iter 210 value 146.503368
## iter 220 value 142.046155
## iter 230 value 137.918023
## iter 240 value 129.807876
## iter 250 value 124.166018
## iter 260 value 121.593737
## iter 270 value 117.778357
## iter 280 value 116.175051
## iter 290 value 114.061840
## iter 300 value 110.921231
## iter 310 value 108.160438
## iter 320 value 105.976961
## iter 330 value 104.270152
## iter 340 value 103.047679
## iter 350 value 102.405006
## iter 360 value 101.453776
## iter 370 value 101.113202
## iter 380 value 100.903903
## iter 390 value 100.422309
## iter 400 value 99.945555
## iter 410 value 98.730365
## iter 420 value 97.335009
## iter 430 value 96.067677
## iter 440 value 95.140918
## iter 450 value 94.623030
## iter 460 value 93.966223
## iter 470 value 93.129303
## iter 480 value 92.251014
## iter 490 value 91.061222
## iter 500 value 89.599591
## final  value 89.599591 
## stopped after 500 iterations
## # weights:  241
## initial  value 1404151.633095 
## iter  10 value 1215.141017
## iter  20 value 758.976340
## iter  30 value 636.059599
## iter  40 value 539.501681
## iter  50 value 404.964221
## iter  60 value 329.502812
## iter  70 value 282.179854
## iter  80 value 251.846050
## iter  90 value 217.843289
## iter 100 value 190.685503
## iter 110 value 159.314775
## iter 120 value 140.570505
## iter 130 value 129.907216
## iter 140 value 120.152925
## iter 150 value 108.885045
## iter 160 value 100.710033
## iter 170 value 93.269918
## iter 180 value 86.415963
## iter 190 value 80.429147
## iter 200 value 75.051106
## iter 210 value 69.627941
## iter 220 value 64.623830
## iter 230 value 61.335661
## iter 240 value 57.872329
## iter 250 value 54.520209
## iter 260 value 51.124032
## iter 270 value 48.729867
## iter 280 value 47.036047
## iter 290 value 45.766156
## iter 300 value 44.547663
## iter 310 value 42.450448
## iter 320 value 40.365933
## iter 330 value 39.055769
## iter 340 value 37.891417
## iter 350 value 36.772243
## iter 360 value 35.741404
## iter 370 value 34.446509
## iter 380 value 33.192121
## iter 390 value 31.808629
## iter 400 value 30.780333
## iter 410 value 30.060431
## iter 420 value 29.603948
## iter 430 value 29.110078
## iter 440 value 28.337075
## iter 450 value 27.858182
## iter 460 value 27.598151
## iter 470 value 27.283517
## iter 480 value 26.601091
## iter 490 value 26.453481
## iter 500 value 26.416410
## final  value 26.416410 
## stopped after 500 iterations
## # weights:  25
## initial  value 1416989.041823 
## iter  10 value 2960.777171
## iter  20 value 1719.661239
## iter  30 value 1410.996786
## iter  40 value 1249.601392
## iter  50 value 1185.450602
## iter  60 value 1174.047284
## iter  70 value 1160.482656
## iter  80 value 1133.990520
## iter  90 value 1042.532070
## iter 100 value 963.746576
## iter 110 value 946.125407
## iter 120 value 939.834232
## iter 130 value 916.539892
## iter 140 value 916.169692
## iter 150 value 915.643496
## iter 160 value 915.563343
## iter 170 value 915.561790
## iter 180 value 915.548558
## iter 190 value 915.479505
## iter 200 value 915.465998
## final  value 915.465940 
## converged
## # weights:  61
## initial  value 1376621.149496 
## iter  10 value 16065.394452
## iter  20 value 13781.148611
## iter  30 value 7675.871127
## iter  40 value 4916.573484
## iter  50 value 4012.861697
## iter  60 value 3534.644192
## iter  70 value 3392.922666
## iter  80 value 3372.988033
## iter  90 value 3281.374845
## iter 100 value 2928.796444
## iter 110 value 2719.223246
## iter 120 value 2589.669803
## iter 130 value 2443.890697
## iter 140 value 2111.945079
## iter 150 value 1636.519692
## iter 160 value 1445.032852
## iter 170 value 1338.128519
## iter 180 value 1320.020574
## iter 190 value 1291.274325
## iter 200 value 1232.413561
## iter 210 value 1029.095621
## iter 220 value 971.659463
## iter 230 value 950.468630
## iter 240 value 933.611948
## iter 250 value 914.779687
## iter 260 value 900.079159
## iter 270 value 892.927943
## iter 280 value 888.280447
## iter 290 value 885.154634
## iter 300 value 884.374327
## iter 310 value 883.577239
## iter 320 value 883.563140
## iter 330 value 883.461320
## iter 340 value 882.870279
## iter 350 value 882.840623
## iter 360 value 882.699917
## iter 370 value 881.846974
## iter 380 value 881.669647
## iter 390 value 878.787846
## iter 400 value 877.665008
## iter 410 value 876.869440
## iter 420 value 876.093261
## iter 430 value 875.652578
## iter 440 value 875.408594
## iter 450 value 875.267781
## iter 460 value 874.215600
## iter 470 value 874.067983
## iter 480 value 873.997632
## iter 490 value 873.828343
## iter 500 value 873.827342
## final  value 873.827342 
## stopped after 500 iterations
## # weights:  121
## initial  value 1447298.929750 
## iter  10 value 5181.929340
## iter  20 value 1834.352568
## iter  30 value 1225.653568
## iter  40 value 1004.525538
## iter  50 value 780.880576
## iter  60 value 671.151369
## iter  70 value 618.964004
## iter  80 value 585.118213
## iter  90 value 567.470666
## iter 100 value 546.258135
## iter 110 value 521.214113
## iter 120 value 498.475328
## iter 130 value 488.175707
## iter 140 value 484.194106
## iter 150 value 477.805818
## iter 160 value 472.216789
## iter 170 value 466.072367
## iter 180 value 460.034552
## iter 190 value 457.442810
## iter 200 value 454.922184
## iter 210 value 453.047270
## iter 220 value 451.910727
## iter 230 value 451.326885
## iter 240 value 451.028485
## iter 250 value 450.966261
## iter 260 value 450.913724
## iter 270 value 450.797581
## iter 280 value 450.592101
## iter 290 value 450.024132
## iter 300 value 449.604921
## iter 310 value 449.253676
## iter 320 value 447.676271
## iter 330 value 445.942952
## iter 340 value 444.375442
## iter 350 value 443.058276
## iter 360 value 442.127030
## iter 370 value 441.366972
## iter 380 value 440.866502
## iter 390 value 440.656359
## iter 400 value 440.230960
## iter 410 value 439.640559
## iter 420 value 437.857674
## iter 430 value 435.193435
## iter 440 value 431.927080
## iter 450 value 427.592689
## iter 460 value 423.121874
## iter 470 value 418.625128
## iter 480 value 415.019988
## iter 490 value 413.482492
## iter 500 value 413.303608
## final  value 413.303608 
## stopped after 500 iterations
## # weights:  181
## initial  value 1401242.562861 
## iter  10 value 1038.030963
## iter  20 value 806.540386
## iter  30 value 653.029115
## iter  40 value 528.121089
## iter  50 value 430.921571
## iter  60 value 378.874183
## iter  70 value 345.569717
## iter  80 value 297.186706
## iter  90 value 246.651319
## iter 100 value 223.122651
## iter 110 value 202.088889
## iter 120 value 190.704742
## iter 130 value 177.292105
## iter 140 value 166.059858
## iter 150 value 153.857644
## iter 160 value 144.155739
## iter 170 value 136.236600
## iter 180 value 130.594237
## iter 190 value 126.433891
## iter 200 value 121.004456
## iter 210 value 118.081121
## iter 220 value 114.939139
## iter 230 value 112.865808
## iter 240 value 110.623610
## iter 250 value 107.852941
## iter 260 value 105.519629
## iter 270 value 102.793834
## iter 280 value 98.091342
## iter 290 value 94.892848
## iter 300 value 93.706192
## iter 310 value 92.467865
## iter 320 value 91.528801
## iter 330 value 90.743983
## iter 340 value 89.936034
## iter 350 value 89.118237
## iter 360 value 88.710885
## iter 370 value 88.563861
## iter 380 value 88.503849
## iter 390 value 88.417194
## iter 400 value 88.335198
## iter 410 value 88.151429
## iter 420 value 87.745279
## iter 430 value 86.792391
## iter 440 value 85.714815
## iter 450 value 84.714625
## iter 460 value 83.814283
## iter 470 value 83.222364
## iter 480 value 82.792683
## iter 490 value 82.264200
## iter 500 value 81.437220
## final  value 81.437220 
## stopped after 500 iterations
## # weights:  241
## initial  value 1383509.087273 
## iter  10 value 1553.884628
## iter  20 value 808.961596
## iter  30 value 616.847233
## iter  40 value 519.128414
## iter  50 value 429.490467
## iter  60 value 361.784154
## iter  70 value 311.438305
## iter  80 value 275.671889
## iter  90 value 241.221776
## iter 100 value 206.198722
## iter 110 value 180.662010
## iter 120 value 158.228139
## iter 130 value 141.208696
## iter 140 value 130.266598
## iter 150 value 121.066071
## iter 160 value 115.024752
## iter 170 value 109.791446
## iter 180 value 104.259469
## iter 190 value 98.988901
## iter 200 value 94.028299
## iter 210 value 87.650714
## iter 220 value 83.727167
## iter 230 value 79.972966
## iter 240 value 76.312975
## iter 250 value 73.292122
## iter 260 value 70.790747
## iter 270 value 68.651743
## iter 280 value 66.599493
## iter 290 value 64.516045
## iter 300 value 61.447797
## iter 310 value 58.357381
## iter 320 value 56.272259
## iter 330 value 54.063302
## iter 340 value 51.591521
## iter 350 value 49.956701
## iter 360 value 47.533085
## iter 370 value 44.453532
## iter 380 value 42.270522
## iter 390 value 40.604199
## iter 400 value 39.091190
## iter 410 value 37.786348
## iter 420 value 36.738922
## iter 430 value 35.781893
## iter 440 value 34.683175
## iter 450 value 33.327122
## iter 460 value 32.091603
## iter 470 value 30.910393
## iter 480 value 29.783656
## iter 490 value 29.194707
## iter 500 value 28.986760
## final  value 28.986760 
## stopped after 500 iterations
## # weights:  25
## initial  value 1402566.442260 
## iter  10 value 6726.057403
## iter  20 value 5552.715900
## iter  30 value 4708.597370
## iter  40 value 2770.514225
## iter  50 value 1686.500964
## iter  60 value 1470.549296
## iter  70 value 1372.380283
## iter  80 value 1351.850000
## iter  90 value 1306.243563
## iter 100 value 1231.538963
## iter 110 value 1203.274197
## iter 120 value 1201.192130
## iter 130 value 1200.798648
## iter 140 value 1199.616964
## final  value 1199.434426 
## converged
## # weights:  61
## initial  value 1394755.557612 
## iter  10 value 8782.134814
## iter  20 value 8243.082055
## iter  30 value 6559.537649
## iter  40 value 4298.018993
## iter  50 value 2835.537276
## iter  60 value 2146.314991
## iter  70 value 1765.966002
## iter  80 value 1601.639379
## iter  90 value 1422.313760
## iter 100 value 1269.018337
## iter 110 value 1192.177176
## iter 120 value 1144.823838
## iter 130 value 1102.857989
## iter 140 value 1057.570147
## iter 150 value 1008.799150
## iter 160 value 975.248580
## iter 170 value 968.210946
## iter 180 value 954.774144
## iter 190 value 937.718569
## iter 200 value 915.595762
## iter 210 value 898.296674
## iter 220 value 885.778288
## iter 230 value 869.773952
## iter 240 value 866.164853
## iter 250 value 861.960647
## iter 260 value 858.232510
## iter 270 value 857.375740
## iter 280 value 856.974669
## iter 290 value 856.915007
## iter 300 value 856.889117
## iter 310 value 856.871027
## iter 320 value 856.857169
## iter 330 value 856.850935
## final  value 856.850046 
## converged
## # weights:  121
## initial  value 1367477.747105 
## iter  10 value 1768.419131
## iter  20 value 1203.581706
## iter  30 value 931.875918
## iter  40 value 825.073254
## iter  50 value 770.759876
## iter  60 value 709.900773
## iter  70 value 669.606119
## iter  80 value 642.202656
## iter  90 value 627.880234
## iter 100 value 614.746524
## iter 110 value 599.277602
## iter 120 value 588.394684
## iter 130 value 583.114386
## iter 140 value 578.624098
## iter 150 value 572.087223
## iter 160 value 560.149334
## iter 170 value 552.073114
## iter 180 value 540.965897
## iter 190 value 535.546346
## iter 200 value 532.272992
## iter 210 value 529.138767
## iter 220 value 526.869453
## iter 230 value 525.768212
## iter 240 value 524.709287
## iter 250 value 524.140692
## iter 260 value 523.876788
## iter 270 value 523.183766
## iter 280 value 522.772033
## iter 290 value 522.398290
## iter 300 value 522.188404
## iter 310 value 522.088319
## iter 320 value 522.057748
## iter 330 value 522.053319
## iter 340 value 522.052875
## final  value 522.052853 
## converged
## # weights:  181
## initial  value 1417207.166820 
## iter  10 value 1235.252851
## iter  20 value 917.488588
## iter  30 value 799.594353
## iter  40 value 715.246819
## iter  50 value 615.962775
## iter  60 value 547.874749
## iter  70 value 522.468032
## iter  80 value 499.184157
## iter  90 value 472.208772
## iter 100 value 454.838902
## iter 110 value 447.235990
## iter 120 value 441.005401
## iter 130 value 435.684474
## iter 140 value 426.121210
## iter 150 value 419.383170
## iter 160 value 413.593081
## iter 170 value 408.060037
## iter 180 value 404.569289
## iter 190 value 398.981785
## iter 200 value 393.913025
## iter 210 value 389.488416
## iter 220 value 383.621049
## iter 230 value 379.795020
## iter 240 value 376.560243
## iter 250 value 373.854890
## iter 260 value 370.938757
## iter 270 value 369.386121
## iter 280 value 367.764458
## iter 290 value 366.564922
## iter 300 value 365.856319
## iter 310 value 365.301535
## iter 320 value 364.833518
## iter 330 value 364.405970
## iter 340 value 364.214612
## iter 350 value 364.049594
## iter 360 value 363.953957
## iter 370 value 363.911548
## iter 380 value 363.860473
## iter 390 value 363.771026
## iter 400 value 363.669217
## iter 410 value 363.630900
## iter 420 value 363.612402
## iter 430 value 363.601128
## iter 440 value 363.579392
## iter 450 value 363.544510
## iter 460 value 363.488872
## iter 470 value 363.409988
## iter 480 value 363.323867
## iter 490 value 363.268130
## iter 500 value 363.136861
## final  value 363.136861 
## stopped after 500 iterations
## # weights:  241
## initial  value 1393326.487461 
## iter  10 value 1313.639497
## iter  20 value 962.409426
## iter  30 value 794.591473
## iter  40 value 683.057396
## iter  50 value 583.167900
## iter  60 value 530.227590
## iter  70 value 505.074253
## iter  80 value 493.361203
## iter  90 value 471.146325
## iter 100 value 448.178492
## iter 110 value 431.630237
## iter 120 value 419.784483
## iter 130 value 411.014992
## iter 140 value 405.396440
## iter 150 value 401.964208
## iter 160 value 398.719022
## iter 170 value 395.288160
## iter 180 value 392.278610
## iter 190 value 388.256973
## iter 200 value 384.128400
## iter 210 value 381.475652
## iter 220 value 379.498014
## iter 230 value 376.607533
## iter 240 value 373.676958
## iter 250 value 369.405717
## iter 260 value 365.629887
## iter 270 value 363.285642
## iter 280 value 361.708804
## iter 290 value 359.031965
## iter 300 value 356.438822
## iter 310 value 354.415395
## iter 320 value 351.676388
## iter 330 value 348.446662
## iter 340 value 346.435856
## iter 350 value 344.319565
## iter 360 value 343.303791
## iter 370 value 342.487341
## iter 380 value 341.799617
## iter 390 value 341.248887
## iter 400 value 340.657747
## iter 410 value 340.277499
## iter 420 value 340.029442
## iter 430 value 339.886577
## iter 440 value 339.738245
## iter 450 value 339.585764
## iter 460 value 339.383795
## iter 470 value 339.214297
## iter 480 value 338.835534
## iter 490 value 338.443343
## iter 500 value 338.019234
## final  value 338.019234 
## stopped after 500 iterations
## # weights:  25
## initial  value 1431258.854456 
## iter  10 value 6446.527863
## iter  20 value 5131.989858
## iter  30 value 2210.026693
## iter  40 value 1332.912023
## iter  50 value 1141.008505
## iter  60 value 1134.657501
## iter  70 value 1127.787548
## iter  80 value 1092.072780
## iter  90 value 1076.092078
## iter 100 value 1066.317952
## iter 110 value 1062.935047
## iter 120 value 1061.219973
## iter 130 value 1052.143180
## iter 140 value 1042.354242
## iter 150 value 1035.814338
## iter 160 value 1030.676737
## iter 170 value 1029.728286
## iter 180 value 1021.022758
## iter 190 value 1004.381999
## iter 200 value 998.228276
## iter 210 value 997.006664
## iter 220 value 996.755801
## iter 230 value 996.272145
## iter 240 value 995.895654
## final  value 995.880378 
## converged
## # weights:  61
## initial  value 1362357.540075 
## iter  10 value 14388.108612
## iter  20 value 3501.711018
## iter  30 value 2363.539527
## iter  40 value 2008.279237
## iter  50 value 1917.222002
## iter  60 value 1898.000094
## iter  70 value 1885.481407
## iter  80 value 1863.525315
## iter  90 value 1832.443887
## iter 100 value 1823.424649
## iter 110 value 1810.300013
## iter 120 value 1801.385356
## iter 130 value 1791.231213
## iter 140 value 1770.002519
## iter 150 value 1723.330424
## iter 160 value 1704.583613
## iter 170 value 1613.491639
## iter 180 value 1476.279016
## iter 190 value 1315.588124
## iter 200 value 1237.454027
## iter 210 value 1168.856329
## iter 220 value 1085.479279
## iter 230 value 896.533056
## iter 240 value 852.457279
## iter 250 value 843.841810
## iter 260 value 836.731731
## iter 270 value 832.622915
## iter 280 value 829.875713
## iter 290 value 826.180427
## iter 300 value 824.921245
## iter 310 value 824.708015
## iter 320 value 824.617284
## iter 330 value 824.566559
## iter 340 value 824.557856
## iter 350 value 824.554891
## final  value 824.554855 
## converged
## # weights:  121
## initial  value 1400052.966310 
## iter  10 value 1358.152484
## iter  20 value 956.669897
## iter  30 value 786.586239
## iter  40 value 669.661695
## iter  50 value 624.628483
## iter  60 value 562.752924
## iter  70 value 520.002211
## iter  80 value 484.749751
## iter  90 value 454.630069
## iter 100 value 437.432488
## iter 110 value 424.946836
## iter 120 value 411.982118
## iter 130 value 390.184380
## iter 140 value 356.703759
## iter 150 value 340.874754
## iter 160 value 327.440394
## iter 170 value 320.388599
## iter 180 value 315.438669
## iter 190 value 310.148328
## iter 200 value 306.089038
## iter 210 value 302.118872
## iter 220 value 299.890431
## iter 230 value 298.899981
## iter 240 value 297.816805
## iter 250 value 297.517901
## iter 260 value 297.394638
## iter 270 value 297.133653
## iter 280 value 296.595771
## iter 290 value 296.218396
## iter 300 value 295.530583
## iter 310 value 295.095074
## iter 320 value 294.553899
## iter 330 value 294.268125
## iter 340 value 293.420659
## iter 350 value 292.639899
## iter 360 value 292.229661
## iter 370 value 292.067593
## iter 380 value 291.942772
## iter 390 value 291.919576
## iter 400 value 291.864885
## iter 410 value 291.694832
## iter 420 value 291.354773
## iter 430 value 290.790256
## iter 440 value 289.988434
## iter 450 value 286.438100
## iter 460 value 282.340856
## iter 470 value 279.829645
## iter 480 value 275.825917
## iter 490 value 273.681374
## iter 500 value 270.043666
## final  value 270.043666 
## stopped after 500 iterations
## # weights:  181
## initial  value 1397145.173967 
## iter  10 value 1107.342303
## iter  20 value 802.300054
## iter  30 value 622.039376
## iter  40 value 505.018136
## iter  50 value 383.322579
## iter  60 value 321.872358
## iter  70 value 292.057257
## iter  80 value 264.664662
## iter  90 value 244.878589
## iter 100 value 227.397092
## iter 110 value 210.518652
## iter 120 value 199.283136
## iter 130 value 191.909072
## iter 140 value 186.580358
## iter 150 value 180.252277
## iter 160 value 173.348722
## iter 170 value 167.986013
## iter 180 value 163.550617
## iter 190 value 160.015087
## iter 200 value 157.194610
## iter 210 value 154.356460
## iter 220 value 151.091332
## iter 230 value 147.863574
## iter 240 value 144.117418
## iter 250 value 141.648148
## iter 260 value 139.229020
## iter 270 value 136.914397
## iter 280 value 134.253191
## iter 290 value 131.435408
## iter 300 value 127.388204
## iter 310 value 123.358414
## iter 320 value 121.079948
## iter 330 value 118.895285
## iter 340 value 117.190109
## iter 350 value 115.735863
## iter 360 value 113.921012
## iter 370 value 112.398862
## iter 380 value 111.998585
## iter 390 value 111.362612
## iter 400 value 110.281182
## iter 410 value 109.077356
## iter 420 value 108.037110
## iter 430 value 106.672610
## iter 440 value 105.530149
## iter 450 value 104.553714
## iter 460 value 103.972618
## iter 470 value 103.332652
## iter 480 value 102.070542
## iter 490 value 100.977154
## iter 500 value 99.897462
## final  value 99.897462 
## stopped after 500 iterations
## # weights:  241
## initial  value 1458997.687732 
## iter  10 value 1728.909875
## iter  20 value 886.809325
## iter  30 value 639.532400
## iter  40 value 459.939493
## iter  50 value 369.594574
## iter  60 value 320.892189
## iter  70 value 276.442183
## iter  80 value 226.768651
## iter  90 value 191.459109
## iter 100 value 166.762053
## iter 110 value 143.849439
## iter 120 value 125.852838
## iter 130 value 113.186592
## iter 140 value 103.053678
## iter 150 value 93.675131
## iter 160 value 86.154930
## iter 170 value 81.545408
## iter 180 value 76.731857
## iter 190 value 71.212244
## iter 200 value 64.687899
## iter 210 value 60.875334
## iter 220 value 57.906336
## iter 230 value 55.642340
## iter 240 value 53.760389
## iter 250 value 51.773653
## iter 260 value 50.147536
## iter 270 value 48.197742
## iter 280 value 46.619925
## iter 290 value 45.380291
## iter 300 value 44.538232
## iter 310 value 43.750938
## iter 320 value 42.976834
## iter 330 value 42.371725
## iter 340 value 41.697246
## iter 350 value 41.001152
## iter 360 value 40.451780
## iter 370 value 39.907713
## iter 380 value 39.408045
## iter 390 value 38.990155
## iter 400 value 38.683983
## iter 410 value 38.117336
## iter 420 value 37.680795
## iter 430 value 37.319155
## iter 440 value 36.919405
## iter 450 value 36.486322
## iter 460 value 36.049742
## iter 470 value 35.597777
## iter 480 value 35.314366
## iter 490 value 35.188518
## iter 500 value 35.136736
## final  value 35.136736 
## stopped after 500 iterations
## # weights:  25
## initial  value 1383411.363470 
## final  value 16085.869356 
## converged
## # weights:  61
## initial  value 1409907.358667 
## iter  10 value 5757.509573
## iter  20 value 2958.999097
## iter  30 value 1862.493172
## iter  40 value 1505.499224
## iter  50 value 1098.572014
## iter  60 value 929.665402
## iter  70 value 880.389936
## iter  80 value 864.287927
## iter  90 value 855.777558
## iter 100 value 848.438593
## iter 110 value 844.104589
## iter 120 value 838.711053
## iter 130 value 830.271948
## iter 140 value 828.759734
## iter 150 value 827.957352
## iter 160 value 825.366560
## iter 170 value 822.013926
## iter 180 value 818.370599
## iter 190 value 816.937676
## iter 200 value 815.836712
## iter 210 value 815.297366
## iter 220 value 814.956971
## iter 230 value 811.241229
## iter 240 value 808.534521
## iter 250 value 807.247274
## iter 260 value 806.748632
## iter 270 value 806.371011
## iter 280 value 806.039333
## iter 290 value 805.762902
## iter 300 value 805.559719
## iter 310 value 805.367140
## iter 320 value 805.333624
## final  value 805.332058 
## converged
## # weights:  121
## initial  value 1405209.960984 
## iter  10 value 1509.899575
## iter  20 value 949.955615
## iter  30 value 808.573193
## iter  40 value 686.731264
## iter  50 value 624.805810
## iter  60 value 584.892478
## iter  70 value 532.726886
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## iter 320 value 276.534463
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## iter 470 value 256.090351
## iter 480 value 255.403429
## iter 490 value 255.217935
## iter 500 value 255.211877
## final  value 255.211877 
## stopped after 500 iterations
## # weights:  181
## initial  value 1375284.441241 
## iter  10 value 1210.986870
## iter  20 value 837.008587
## iter  30 value 698.629485
## iter  40 value 563.678871
## iter  50 value 453.096069
## iter  60 value 405.928706
## iter  70 value 367.335606
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## iter  90 value 299.945854
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## iter 480 value 120.826781
## iter 490 value 119.777479
## iter 500 value 118.806467
## final  value 118.806467 
## stopped after 500 iterations
## # weights:  241
## initial  value 1406530.155692 
## iter  10 value 1439.726198
## iter  20 value 791.240007
## iter  30 value 598.083621
## iter  40 value 498.740921
## iter  50 value 421.953437
## iter  60 value 345.241243
## iter  70 value 303.421895
## iter  80 value 266.099143
## iter  90 value 235.536732
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## iter 470 value 22.556062
## iter 480 value 22.118417
## iter 490 value 21.882705
## iter 500 value 21.790990
## final  value 21.790990 
## stopped after 500 iterations
## # weights:  25
## initial  value 1424522.319439 
## iter  10 value 4265.589684
## iter  20 value 2397.472156
## iter  30 value 2129.302172
## iter  40 value 1608.055140
## iter  50 value 1468.965959
## iter  60 value 1404.871251
## iter  70 value 1218.635798
## iter  80 value 1109.838412
## iter  90 value 1049.670285
## iter 100 value 971.956837
## iter 110 value 960.584457
## iter 120 value 942.694225
## iter 130 value 927.482914
## iter 140 value 923.242571
## iter 150 value 921.525523
## iter 160 value 921.440835
## iter 170 value 919.807091
## iter 180 value 918.339304
## iter 190 value 917.247269
## iter 200 value 916.240658
## iter 210 value 916.223135
## iter 220 value 916.148160
## iter 230 value 915.938819
## iter 240 value 915.616802
## iter 250 value 915.071791
## iter 260 value 915.055467
## iter 270 value 914.981239
## iter 280 value 914.856131
## iter 290 value 914.834969
## iter 300 value 914.614932
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## iter 350 value 914.486810
## iter 360 value 914.333379
## iter 370 value 914.326333
## iter 370 value 914.326329
## final  value 914.326197 
## converged
## # weights:  61
## initial  value 1388300.358808 
## iter  10 value 5033.280956
## iter  20 value 2107.840865
## iter  30 value 1510.666890
## iter  40 value 1308.792492
## iter  50 value 1167.764175
## iter  60 value 1055.677025
## iter  70 value 939.585423
## iter  80 value 829.121075
## iter  90 value 761.371244
## iter 100 value 740.853963
## iter 110 value 724.138386
## iter 120 value 698.322843
## iter 130 value 683.876771
## iter 140 value 676.076380
## iter 150 value 669.963127
## iter 160 value 662.146896
## iter 170 value 657.151312
## iter 180 value 649.835304
## iter 190 value 643.244721
## iter 200 value 639.811689
## iter 210 value 637.573599
## iter 220 value 636.185074
## iter 230 value 634.782671
## iter 240 value 631.726444
## iter 250 value 629.611682
## iter 260 value 629.488176
## iter 270 value 629.154273
## iter 280 value 628.560394
## iter 290 value 628.171302
## iter 300 value 628.019045
## iter 310 value 627.847650
## iter 320 value 627.821612
## iter 330 value 627.804271
## iter 340 value 627.761719
## iter 350 value 627.691731
## iter 360 value 627.584353
## iter 370 value 627.454028
## iter 380 value 627.452650
## iter 390 value 627.451976
## iter 400 value 627.451259
## iter 410 value 627.450196
## iter 420 value 627.440710
## iter 430 value 627.422308
## iter 440 value 627.420469
## iter 450 value 627.399582
## iter 460 value 627.398195
## iter 460 value 627.398190
## iter 460 value 627.398190
## final  value 627.398190 
## converged
## # weights:  121
## initial  value 1387699.685308 
## iter  10 value 4442.084922
## iter  20 value 1701.262215
## iter  30 value 1099.327612
## iter  40 value 890.022221
## iter  50 value 826.340952
## iter  60 value 762.613543
## iter  70 value 740.161750
## iter  80 value 726.367719
## iter  90 value 722.096916
## iter 100 value 715.322975
## iter 110 value 712.352899
## iter 120 value 709.750440
## iter 130 value 709.358158
## iter 140 value 706.982066
## iter 150 value 702.152951
## iter 160 value 697.395928
## iter 170 value 688.453447
## iter 180 value 681.988238
## iter 190 value 676.272427
## iter 200 value 658.810014
## iter 210 value 645.696516
## iter 220 value 642.904186
## iter 230 value 640.408646
## iter 240 value 637.973011
## iter 250 value 635.218009
## iter 260 value 634.917775
## iter 270 value 633.407233
## iter 280 value 631.186134
## iter 290 value 627.761696
## iter 300 value 626.665449
## iter 310 value 626.046237
## iter 320 value 625.757689
## iter 330 value 625.591667
## iter 340 value 625.471784
## iter 350 value 625.395708
## iter 360 value 625.357167
## iter 370 value 625.314095
## iter 380 value 625.285399
## iter 390 value 625.215867
## iter 400 value 625.200467
## iter 410 value 623.252363
## iter 420 value 617.421335
## iter 430 value 612.639363
## iter 440 value 611.078901
## iter 450 value 609.006449
## iter 460 value 608.418468
## iter 470 value 607.992970
## iter 480 value 607.767551
## iter 490 value 607.585468
## iter 500 value 607.483476
## final  value 607.483476 
## stopped after 500 iterations
## # weights:  181
## initial  value 1304399.167845 
## iter  10 value 1141.288055
## iter  20 value 831.013472
## iter  30 value 672.990045
## iter  40 value 550.573160
## iter  50 value 432.802596
## iter  60 value 371.654068
## iter  70 value 324.210511
## iter  80 value 279.891800
## iter  90 value 253.076729
## iter 100 value 233.777589
## iter 110 value 218.600899
## iter 120 value 207.641212
## iter 130 value 198.577162
## iter 140 value 188.303842
## iter 150 value 179.245015
## iter 160 value 174.077584
## iter 170 value 168.610556
## iter 180 value 162.243985
## iter 190 value 155.118536
## iter 200 value 149.944819
## iter 210 value 144.145353
## iter 220 value 137.860236
## iter 230 value 131.067389
## iter 240 value 124.962772
## iter 250 value 120.114394
## iter 260 value 115.821158
## iter 270 value 112.115071
## iter 280 value 109.166425
## iter 290 value 106.931589
## iter 300 value 104.130669
## iter 310 value 101.861303
## iter 320 value 100.814380
## iter 330 value 99.545426
## iter 340 value 98.023952
## iter 350 value 96.433503
## iter 360 value 95.712559
## iter 370 value 95.561295
## iter 380 value 95.496647
## iter 390 value 95.388732
## iter 400 value 95.254251
## iter 410 value 95.128698
## iter 420 value 94.967333
## iter 430 value 94.858961
## iter 440 value 94.675341
## iter 450 value 94.359018
## iter 460 value 93.091568
## iter 470 value 92.117462
## iter 480 value 91.815564
## iter 490 value 91.421153
## iter 500 value 91.081678
## final  value 91.081678 
## stopped after 500 iterations
## # weights:  241
## initial  value 1494792.852562 
## iter  10 value 1650.365895
## iter  20 value 881.202073
## iter  30 value 620.063194
## iter  40 value 443.105554
## iter  50 value 352.457176
## iter  60 value 307.924822
## iter  70 value 267.342908
## iter  80 value 240.187042
## iter  90 value 209.420846
## iter 100 value 189.291861
## iter 110 value 172.084488
## iter 120 value 159.109883
## iter 130 value 147.861887
## iter 140 value 138.022472
## iter 150 value 128.825125
## iter 160 value 122.719523
## iter 170 value 117.736931
## iter 180 value 113.139305
## iter 190 value 108.260288
## iter 200 value 102.292462
## iter 210 value 97.274966
## iter 220 value 93.474227
## iter 230 value 88.978878
## iter 240 value 84.267821
## iter 250 value 78.416314
## iter 260 value 73.580207
## iter 270 value 68.086465
## iter 280 value 64.327722
## iter 290 value 61.779981
## iter 300 value 59.467241
## iter 310 value 57.626092
## iter 320 value 55.925421
## iter 330 value 54.086083
## iter 340 value 52.295793
## iter 350 value 50.624234
## iter 360 value 48.828349
## iter 370 value 47.917478
## iter 380 value 47.130496
## iter 390 value 46.414324
## iter 400 value 45.767326
## iter 410 value 44.977099
## iter 420 value 44.021037
## iter 430 value 43.173394
## iter 440 value 41.905073
## iter 450 value 40.429950
## iter 460 value 39.064303
## iter 470 value 38.066126
## iter 480 value 37.133087
## iter 490 value 36.819597
## iter 500 value 36.730349
## final  value 36.730349 
## stopped after 500 iterations
## # weights:  25
## initial  value 1536496.891690 
## iter  10 value 21717.180299
## iter  20 value 16517.954314
## iter  30 value 14853.986863
## iter  40 value 9493.319339
## iter  50 value 3352.471783
## iter  60 value 2546.656437
## iter  70 value 2362.381514
## iter  80 value 2144.918770
## iter  90 value 1772.315155
## iter 100 value 1727.300659
## iter 110 value 1603.895751
## iter 120 value 1529.043363
## iter 130 value 1517.249470
## iter 140 value 1517.047736
## iter 140 value 1517.047735
## final  value 1517.047735 
## converged
##################################
# Reporting the apparent results
# for the NN model
##################################
NN_DALEX <- DALEX::explain(NN_Tune,
                           data = MD.Model.Predictors,
                           y = MD$LIFEXP,
                           verbose = FALSE,
                           label = "NN")

(NN_DALEX_Performance <- model_performance(NN_DALEX))
## Measures for:  regression
## mse        : 3.792286 
## rmse       : 1.947379 
## r2         : 0.9387186 
## mad        : 1.118942
## 
## Residuals:
##         0%        10%        20%        30%        40%        50%        60% 
## -6.7019930 -2.2284111 -1.3596353 -0.7496302 -0.2984808  0.0323945  0.3834313 
##        70%        80%        90%       100% 
##  0.8343248  1.5340057  2.1809442  6.4827220
(NN_DALEX_Diagnostics <- model_diagnostics(NN_DALEX))
##     GENDER              CONTIN       INFMOR           PERCAP       
##  Male  :139   Africa       :89   Min.   :0.3365   Min.   :-1.4775  
##  Female:153   Asia         :73   1st Qu.:1.7047   1st Qu.: 0.6183  
##               Europe       :62   Median :2.6100   Median : 1.7960  
##               North America:31   Mean   :2.5569   Mean   : 1.7571  
##               Oceania      :18   3rd Qu.:3.5025   3rd Qu.: 2.7938  
##               South America:19   Max.   :4.4864   Max.   : 4.7293  
##      CLTECH           NCOMOR            y             y_hat      
##  Min.   :  0.00   Min.   :1.866   Min.   :52.84   Min.   :56.39  
##  1st Qu.: 24.35   1st Qu.:3.944   1st Qu.:66.93   1st Qu.:66.84  
##  Median : 82.80   Median :4.717   Median :73.53   Median :73.71  
##  Mean   : 64.60   Mean   :4.652   Mean   :72.47   Mean   :72.46  
##  3rd Qu.:100.00   3rd Qu.:5.347   3rd Qu.:78.54   3rd Qu.:78.31  
##  Max.   :100.00   Max.   :7.959   Max.   :87.45   Max.   :85.01  
##    residuals       abs_residuals         label                ids        
##  Min.   :-6.7020   Min.   :0.006932   Length:292         Min.   :  1.00  
##  1st Qu.:-0.9466   1st Qu.:0.423443   Class :character   1st Qu.: 73.75  
##  Median : 0.0324   Median :1.118942   Mode  :character   Median :146.50  
##  Mean   : 0.0151   Mean   :1.437687                      Mean   :146.50  
##  3rd Qu.: 1.2088   3rd Qu.:2.042302                      3rd Qu.:219.25  
##  Max.   : 6.4827   Max.   :6.701993                      Max.   :292.00
plot(NN_DALEX_Diagnostics,
     variable = "y",
     yvariable = "y_hat") +
  geom_point(size=3) +
  scale_x_continuous("Observed LIFEXP") +
  scale_y_continuous("Predicted LIFEXP") +
  geom_abline(slope = 1) +
  ggtitle("RF: Observed and Predicted LIFEXP")

NN_DALEX_VariableImportance    <- model_parts(NN_DALEX,
                                              loss_function = loss_root_mean_square,
                                              B = 200,
                                              N = NULL)

plot(NN_DALEX_VariableImportance)

##################################
# Reporting the cross-validation results
# for the NN model
##################################
NN_Tune
## Neural Network 
## 
## 292 samples
##   6 predictor
## 
## Pre-processing: centered (4), scaled (4), ignore (2) 
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 264, 263, 263, 262, 263, 264, ... 
## Resampling results across tuning parameters:
## 
##   size  decay  RMSE      Rsquared   MAE     
##    2    0e+00  2.913158  0.8500380  2.299048
##    2    1e-05  3.316750  0.7817528  2.514331
##    2    1e-04  3.312748  0.9255037  2.623476
##    2    1e-03  2.179988  0.9262000  1.649639
##    2    1e-01  2.070060  0.9321827  1.594778
##    5    0e+00  5.259886  0.8372621  2.275294
##    5    1e-05  3.641720  0.8067295  2.076761
##    5    1e-04  2.622448  0.8956273  1.750379
##    5    1e-03  2.288813  0.9148856  1.683198
##    5    1e-01  2.244542  0.9208400  1.660874
##   10    0e+00  4.090892  0.8342633  2.272149
##   10    1e-05  6.251900  0.6529587  3.162672
##   10    1e-04  3.404514  0.8251865  2.140352
##   10    1e-03  2.828581  0.8793067  2.143924
##   10    1e-01  2.323915  0.9157548  1.729951
##   15    0e+00  4.182909  0.7642496  2.918018
##   15    1e-05  4.129136  0.7596288  2.961311
##   15    1e-04  4.024271  0.7862760  2.876569
##   15    1e-03  3.610424  0.8251640  2.696243
##   15    1e-01  2.543247  0.9030396  1.909596
##   20    0e+00  4.804438  0.7226852  3.660531
##   20    1e-05  4.303982  0.7508849  3.181808
##   20    1e-04  4.909855  0.6993867  3.617313
##   20    1e-03  4.837680  0.6951049  3.487130
##   20    1e-01  2.860518  0.8768559  2.040583
## 
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were size = 2 and decay = 0.1.
NN_Tune$finalModel
## a 10-2-1 network with 25 weights
## inputs: GENDERFemale CONTINAsia CONTINEurope CONTINNorth America CONTINOceania CONTINSouth America INFMOR PERCAP CLTECH NCOMOR 
## output(s): .outcome 
## options were - linear output units  decay=0.1
(NN_Tune_RMSE <- NN_Tune$results[NN_Tune$results$size==NN_Tune$bestTune$size &
                              NN_Tune$results$decay==NN_Tune$bestTune$decay,
                 c("RMSE")])
## [1] 2.07006
(NN_Tune_Rsquared <- NN_Tune$results[NN_Tune$results$size==NN_Tune$bestTune$size &
                              NN_Tune$results$decay==NN_Tune$bestTune$decay,
                 c("Rsquared")])
## [1] 0.9321827
(NN_Tune_MAE <- NN_Tune$results[NN_Tune$results$size==NN_Tune$bestTune$size &
                              NN_Tune$results$decay==NN_Tune$bestTune$decay,
                 c("MAE")])
## [1] 1.594778

1.3.6.4 Partial Least Squares Regression (PLS)


Code Chunk | Output
##################################
# Defining the model hyperparameter values
# for the PLS model
##################################
PLS_Grid = expand.grid(ncomp = 1:5)

##################################
# Running the PLS model
# by setting the caret method to 'pls'
##################################
set.seed(12345678)
PLS_Tune <- train(x = MD.Model.Predictors,
                  y = MD$LIFEXP,
                  method = "pls",
                  tuneGrid = PLS_Grid,
                  trControl = KFold_Control)

##################################
# Reporting the apparent results
# for the PLS model
##################################
PLS_DALEX <- DALEX::explain(PLS_Tune,
                           data = MD.Model.Predictors,
                           y = MD$LIFEXP,
                           verbose = FALSE,
                           label = "PLS")

(PLS_DALEX_Performance <- model_performance(PLS_DALEX))
## Measures for:  regression
## mse        : 5.876756 
## rmse       : 2.424202 
## r2         : 0.9050347 
## mad        : 1.524704
## 
## Residuals:
##         0%        10%        20%        30%        40%        50%        60% 
## -7.8171653 -3.1634756 -1.8497887 -1.1383462 -0.5658555  0.2131137  0.6858102 
##        70%        80%        90%       100% 
##  1.2703500  1.8554207  2.8334884  6.6842346
(PLS_DALEX_Diagnostics <- model_diagnostics(PLS_DALEX))
##     GENDER              CONTIN       INFMOR           PERCAP       
##  Male  :139   Africa       :89   Min.   :0.3365   Min.   :-1.4775  
##  Female:153   Asia         :73   1st Qu.:1.7047   1st Qu.: 0.6183  
##               Europe       :62   Median :2.6100   Median : 1.7960  
##               North America:31   Mean   :2.5569   Mean   : 1.7571  
##               Oceania      :18   3rd Qu.:3.5025   3rd Qu.: 2.7938  
##               South America:19   Max.   :4.4864   Max.   : 4.7293  
##      CLTECH           NCOMOR            y             y_hat      
##  Min.   :  0.00   Min.   :1.866   Min.   :52.84   Min.   :57.87  
##  1st Qu.: 24.35   1st Qu.:3.944   1st Qu.:66.93   1st Qu.:66.38  
##  Median : 82.80   Median :4.717   Median :73.53   Median :73.39  
##  Mean   : 64.60   Mean   :4.652   Mean   :72.47   Mean   :72.47  
##  3rd Qu.:100.00   3rd Qu.:5.347   3rd Qu.:78.54   3rd Qu.:78.18  
##  Max.   :100.00   Max.   :7.959   Max.   :87.45   Max.   :88.65  
##    residuals       abs_residuals        label                ids        
##  Min.   :-7.8172   Min.   :0.01345   Length:292         Min.   :  1.00  
##  1st Qu.:-1.3989   1st Qu.:0.74951   Class :character   1st Qu.: 73.75  
##  Median : 0.2131   Median :1.52470   Mode  :character   Median :146.50  
##  Mean   : 0.0000   Mean   :1.89719                      Mean   :146.50  
##  3rd Qu.: 1.5808   3rd Qu.:2.60138                      3rd Qu.:219.25  
##  Max.   : 6.6842   Max.   :7.81717                      Max.   :292.00
plot(PLS_DALEX_Diagnostics,
     variable = "y",
     yvariable = "y_hat") +
  geom_point(size=3) +
  scale_x_continuous("Observed LIFEXP") +
  scale_y_continuous("Predicted LIFEXP") +
  geom_abline(slope = 1) +
  ggtitle("RF: Observed and Predicted LIFEXP")

PLS_DALEX_VariableImportance    <- model_parts(PLS_DALEX,
                                              loss_function = loss_root_mean_square,
                                              B = 200,
                                              N = NULL)

plot(PLS_DALEX_VariableImportance)

##################################
# Reporting the cross-validation results
# for the PLS model
##################################
PLS_Tune
## Partial Least Squares 
## 
## 292 samples
##   6 predictor
## 
## No pre-processing
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 264, 263, 263, 262, 263, 264, ... 
## Resampling results across tuning parameters:
## 
##   ncomp  RMSE      Rsquared   MAE     
##   1      4.997627  0.6039633  4.073632
##   2      3.251276  0.8416354  2.478243
##   3      2.705139  0.8916091  2.067882
##   4      2.572417  0.9014321  2.006329
##   5      2.463222  0.9087387  1.954148
## 
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was ncomp = 5.
PLS_Tune$finalModel
## Partial least squares regression , fitted with the orthogonal scores algorithm.
## Call:
## plsr(formula = .outcome ~ ., ncomp = ncomp, data = dat, method = "oscorespls")
(PLS_Tune_RMSE <- PLS_Tune$results[PLS_Tune$results$ncomp==PLS_Tune$bestTune$ncomp,
                 c("RMSE")])
## [1] 2.463222
(PLS_Tune_Rsquared <- PLS_Tune$results[PLS_Tune$results$ncomp==PLS_Tune$bestTune$ncomp,
                 c("Rsquared")])
## [1] 0.9087387
(PLS_Tune_MAE <- PLS_Tune$results[PLS_Tune$results$ncomp==PLS_Tune$bestTune$ncomp,
                 c("MAE")])
## [1] 1.954148

1.3.6.5 Cubist Regression (CUBIST)


Code Chunk | Output
##################################
# Defining the model hyperparameter values
# for the CUBIST model
##################################
CUBIST_Grid = expand.grid(committees = c(10, 20, 30, 40, 50),
                          neighbors = c(0, 3, 6, 9))


##################################
# Running the CUBIST model
# by setting the caret method to 'cubist'
##################################
set.seed(12345678)
CUBIST_Tune <- train(x = MD.Model.Predictors,
                   y = MD$LIFEXP,
                   method = "cubist",
                   tuneGrid = CUBIST_Grid,
                   trControl = KFold_Control)

##################################
# Reporting the apparent results
# for the CUBIST model
##################################
CUBIST_DALEX <- DALEX::explain(CUBIST_Tune,
                           data = MD.Model.Predictors,
                           y = MD$LIFEXP,
                           verbose = FALSE,
                           label = "CUBIST")

(CUBIST_DALEX_Performance <- model_performance(CUBIST_DALEX))
## Measures for:  regression
## mse        : 3.658042 
## rmse       : 1.912601 
## r2         : 0.9408879 
## mad        : 1.05533
## 
## Residuals:
##          0%         10%         20%         30%         40%         50% 
## -6.90777771 -2.15534625 -1.41800017 -0.72510388 -0.33359399  0.01040643 
##         60%         70%         80%         90%        100% 
##  0.32413965  0.72477280  1.37141615  2.49934372  6.82557834
(CUBIST_DALEX_Diagnostics <- model_diagnostics(CUBIST_DALEX))
##     GENDER              CONTIN       INFMOR           PERCAP       
##  Male  :139   Africa       :89   Min.   :0.3365   Min.   :-1.4775  
##  Female:153   Asia         :73   1st Qu.:1.7047   1st Qu.: 0.6183  
##               Europe       :62   Median :2.6100   Median : 1.7960  
##               North America:31   Mean   :2.5569   Mean   : 1.7571  
##               Oceania      :18   3rd Qu.:3.5025   3rd Qu.: 2.7938  
##               South America:19   Max.   :4.4864   Max.   : 4.7293  
##      CLTECH           NCOMOR            y             y_hat      
##  Min.   :  0.00   Min.   :1.866   Min.   :52.84   Min.   :55.50  
##  1st Qu.: 24.35   1st Qu.:3.944   1st Qu.:66.93   1st Qu.:66.51  
##  Median : 82.80   Median :4.717   Median :73.53   Median :73.61  
##  Mean   : 64.60   Mean   :4.652   Mean   :72.47   Mean   :72.40  
##  3rd Qu.:100.00   3rd Qu.:5.347   3rd Qu.:78.54   3rd Qu.:78.06  
##  Max.   :100.00   Max.   :7.959   Max.   :87.45   Max.   :86.25  
##    residuals        abs_residuals         label                ids        
##  Min.   :-6.90778   Min.   :0.003691   Length:292         Min.   :  1.00  
##  1st Qu.:-1.06822   1st Qu.:0.447052   Class :character   1st Qu.: 73.75  
##  Median : 0.01041   Median :1.055330   Mode  :character   Median :146.50  
##  Mean   : 0.07148   Mean   :1.409394                      Mean   :146.50  
##  3rd Qu.: 1.00547   3rd Qu.:2.063695                      3rd Qu.:219.25  
##  Max.   : 6.82558   Max.   :6.907778                      Max.   :292.00
plot(CUBIST_DALEX_Diagnostics,
     variable = "y",
     yvariable = "y_hat") +
  geom_point(size=3) +
  scale_x_continuous("Observed LIFEXP") +
  scale_y_continuous("Predicted LIFEXP") +
  geom_abline(slope = 1) +
  ggtitle("RF: Observed and Predicted LIFEXP")

CUBIST_DALEX_VariableImportance    <- model_parts(CUBIST_DALEX,
                                              loss_function = loss_root_mean_square,
                                              B = 200,
                                              N = NULL)

plot(CUBIST_DALEX_VariableImportance)

##################################
# Reporting the cross-validation results
# for the CUBIST model
##################################
CUBIST_Tune
## Cubist 
## 
## 292 samples
##   6 predictor
## 
## No pre-processing
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 264, 263, 263, 262, 263, 264, ... 
## Resampling results across tuning parameters:
## 
##   committees  neighbors  RMSE      Rsquared   MAE     
##   10          0          2.143307  0.9284329  1.597946
##   10          3          2.161575  0.9264713  1.628394
##   10          6          2.135997  0.9293777  1.600862
##   10          9          2.120955  0.9307087  1.593085
##   20          0          2.118571  0.9293801  1.589678
##   20          3          2.171113  0.9255303  1.629055
##   20          6          2.138896  0.9287024  1.597065
##   20          9          2.118487  0.9303570  1.588802
##   30          0          2.104752  0.9307090  1.577201
##   30          3          2.171787  0.9257896  1.634946
##   30          6          2.135237  0.9293559  1.598399
##   30          9          2.113010  0.9311860  1.587534
##   40          0          2.099914  0.9312085  1.569779
##   40          3          2.164063  0.9263082  1.632470
##   40          6          2.126530  0.9299765  1.592498
##   40          9          2.104461  0.9318479  1.580217
##   50          0          2.096744  0.9316609  1.568414
##   50          3          2.158509  0.9267267  1.629668
##   50          6          2.120160  0.9304991  1.588673
##   50          9          2.097972  0.9324203  1.575005
## 
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were committees = 50 and neighbors = 0.
CUBIST_Tune$finalModel
## 
## Call:
## cubist.default(x = x, y = y, committees = param$committees)
## 
## Number of samples: 292 
## Number of predictors: 6 
## 
## Number of committees: 50 
## Number of rules per committee: 6, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2 ...
(CUBIST_Tune_RMSE <- CUBIST_Tune$results[CUBIST_Tune$results$committees==CUBIST_Tune$bestTune$committees &
                              CUBIST_Tune$results$neighbors==CUBIST_Tune$bestTune$neighbors,
                 c("RMSE")])
## [1] 2.096744
(CUBIST_Tune_Rsquared <- CUBIST_Tune$results[CUBIST_Tune$results$committees==CUBIST_Tune$bestTune$committees &
                              CUBIST_Tune$results$neighbors==CUBIST_Tune$bestTune$neighbors,
                 c("Rsquared")])
## [1] 0.9316609
(CUBIST_Tune_MAE <- CUBIST_Tune$results[CUBIST_Tune$results$committees==CUBIST_Tune$bestTune$committees &
                              CUBIST_Tune$results$neighbors==CUBIST_Tune$bestTune$neighbors,
                 c("MAE")])
## [1] 1.568414

1.3.7 Model Performance Validation


Code Chunk | Output
##################################
# Evaluating the models
# on the model test data
##################################

##################################
# Formulating the DALEX object
# for the Best GBM model
# as applied to the model test data
##################################
GBM_DALEX <- DALEX::explain(GBM_Tune,
                            data = MT.Model.Predictors,
                            y = MT$LIFEXP,
                            verbose = FALSE,
                            label = "GBM")

(GBM_DALEX_Performance <- model_performance(GBM_DALEX))
## Measures for:  regression
## mse        : 4.877673 
## rmse       : 2.208545 
## r2         : 0.9084603 
## mad        : 1.155262
## 
## Residuals:
##          0%         10%         20%         30%         40%         50% 
## -4.35206346 -2.32130175 -1.24214793 -0.82877725 -0.46233520  0.06134295 
##         60%         70%         80%         90%        100% 
##  0.59630498  0.73104236  1.75812056  3.45145715  6.41897554
(GBM_DALEX_Diagnostics <- model_diagnostics(GBM_DALEX))
##     GENDER             CONTIN       INFMOR           PERCAP       
##  Male  :43   Africa       :17   Min.   :0.6419   Min.   :-1.4775  
##  Female:29   Asia         :21   1st Qu.:1.7090   1st Qu.: 0.7926  
##              Europe       :16   Median :2.6602   Median : 1.7266  
##              North America:11   Mean   :2.5559   Mean   : 1.7439  
##              Oceania      : 2   3rd Qu.:3.4135   3rd Qu.: 2.8314  
##              South America: 5   Max.   :4.3858   Max.   : 4.4466  
##      CLTECH           NCOMOR            y             y_hat      
##  Min.   :  0.20   Min.   :2.511   Min.   :51.20   Min.   :55.11  
##  1st Qu.: 49.02   1st Qu.:3.887   1st Qu.:67.42   1st Qu.:68.18  
##  Median : 90.20   Median :4.742   Median :73.51   Median :73.02  
##  Mean   : 72.08   Mean   :4.752   Mean   :72.61   Mean   :72.33  
##  3rd Qu.:100.00   3rd Qu.:5.631   3rd Qu.:78.47   3rd Qu.:78.53  
##  Max.   :100.00   Max.   :7.406   Max.   :86.20   Max.   :84.82  
##    residuals        abs_residuals         label                ids       
##  Min.   :-4.35206   Min.   :0.003661   Length:72          Min.   : 1.00  
##  1st Qu.:-1.04139   1st Qu.:0.609636   Class :character   1st Qu.:18.75  
##  Median : 0.06134   Median :1.155262   Mode  :character   Median :36.50  
##  Mean   : 0.27559   Mean   :1.661957                      Mean   :36.50  
##  3rd Qu.: 1.45652   3rd Qu.:2.352673                      3rd Qu.:54.25  
##  Max.   : 6.41898   Max.   :6.418976                      Max.   :72.00
plot(GBM_DALEX_Diagnostics,
     variable = "y",
     yvariable = "y_hat") +
  geom_point(size=3) +
  scale_x_continuous("Observed LIFEXP") +
  scale_y_continuous("Predicted LIFEXP") +
  geom_abline(slope = 1) +
  ggtitle("GBM: Observed and Predicted LIFEXP")

##################################
# Formulating the DALEX object
# for the Best RF model
# as applied to the model test data
##################################
RF_DALEX <- DALEX::explain(RF_Tune,
                           data = MT.Model.Predictors,
                           y = MT$LIFEXP,
                           verbose = FALSE,
                           label = "RF")

(RF_DALEX_Performance <- model_performance(RF_DALEX))
## Measures for:  regression
## mse        : 6.25631 
## rmse       : 2.501262 
## r2         : 0.8825872 
## mad        : 1.594092
## 
## Residuals:
##         0%        10%        20%        30%        40%        50%        60% 
## -7.2080243 -3.0176466 -2.1047296 -1.2247647 -0.2712163  0.1992440  0.7128073 
##        70%        80%        90%       100% 
##  1.1067986  1.7677385  3.1744799  7.4973476
(RF_DALEX_Diagnostics <- model_diagnostics(RF_DALEX))
##     GENDER             CONTIN       INFMOR           PERCAP       
##  Male  :43   Africa       :17   Min.   :0.6419   Min.   :-1.4775  
##  Female:29   Asia         :21   1st Qu.:1.7090   1st Qu.: 0.7926  
##              Europe       :16   Median :2.6602   Median : 1.7266  
##              North America:11   Mean   :2.5559   Mean   : 1.7439  
##              Oceania      : 2   3rd Qu.:3.4135   3rd Qu.: 2.8314  
##              South America: 5   Max.   :4.3858   Max.   : 4.4466  
##      CLTECH           NCOMOR            y             y_hat      
##  Min.   :  0.20   Min.   :2.511   Min.   :51.20   Min.   :54.85  
##  1st Qu.: 49.02   1st Qu.:3.887   1st Qu.:67.42   1st Qu.:68.50  
##  Median : 90.20   Median :4.742   Median :73.51   Median :73.19  
##  Mean   : 72.08   Mean   :4.752   Mean   :72.61   Mean   :72.58  
##  3rd Qu.:100.00   3rd Qu.:5.631   3rd Qu.:78.47   3rd Qu.:78.61  
##  Max.   :100.00   Max.   :7.406   Max.   :86.20   Max.   :84.60  
##    residuals        abs_residuals        label                ids       
##  Min.   :-7.20802   Min.   :0.01395   Length:72          Min.   : 1.00  
##  1st Qu.:-1.69056   1st Qu.:0.73760   Class :character   1st Qu.:18.75  
##  Median : 0.19924   Median :1.59409   Mode  :character   Median :36.50  
##  Mean   : 0.02669   Mean   :1.91950                      Mean   :36.50  
##  3rd Qu.: 1.50013   3rd Qu.:2.74804                      3rd Qu.:54.25  
##  Max.   : 7.49735   Max.   :7.49735                      Max.   :72.00
plot(RF_DALEX_Diagnostics,
     variable = "y",
     yvariable = "y_hat") +
  geom_point(size=3) +
  scale_x_continuous("Observed LIFEXP") +
  scale_y_continuous("Predicted LIFEXP") +
  geom_abline(slope = 1) +
  ggtitle("RF: Observed and Predicted LIFEXP")

##################################
# Formulating the DALEX object
# for the Best NN model
# as applied to the model test data
##################################
NN_DALEX <- DALEX::explain(NN_Tune,
                           data = MT.Model.Predictors,
                           y = MT$LIFEXP,
                           verbose = FALSE,
                           label = "NN")

(NN_DALEX_Performance <- model_performance(NN_DALEX))
## Measures for:  regression
## mse        : 5.300062 
## rmse       : 2.302186 
## r2         : 0.9005332 
## mad        : 1.293056
## 
## Residuals:
##         0%        10%        20%        30%        40%        50%        60% 
## -6.0612264 -2.8063521 -1.3495125 -1.0121289 -0.3547678  0.2159281  0.6061218 
##        70%        80%        90%       100% 
##  1.1206919  1.8356939  2.8346540  7.8217567
(NN_DALEX_Diagnostics <- model_diagnostics(NN_DALEX))
##     GENDER             CONTIN       INFMOR           PERCAP       
##  Male  :43   Africa       :17   Min.   :0.6419   Min.   :-1.4775  
##  Female:29   Asia         :21   1st Qu.:1.7090   1st Qu.: 0.7926  
##              Europe       :16   Median :2.6602   Median : 1.7266  
##              North America:11   Mean   :2.5559   Mean   : 1.7439  
##              Oceania      : 2   3rd Qu.:3.4135   3rd Qu.: 2.8314  
##              South America: 5   Max.   :4.3858   Max.   : 4.4466  
##      CLTECH           NCOMOR            y             y_hat      
##  Min.   :  0.20   Min.   :2.511   Min.   :51.20   Min.   :52.40  
##  1st Qu.: 49.02   1st Qu.:3.887   1st Qu.:67.42   1st Qu.:66.97  
##  Median : 90.20   Median :4.742   Median :73.51   Median :73.22  
##  Mean   : 72.08   Mean   :4.752   Mean   :72.61   Mean   :72.48  
##  3rd Qu.:100.00   3rd Qu.:5.631   3rd Qu.:78.47   3rd Qu.:78.83  
##  Max.   :100.00   Max.   :7.406   Max.   :86.20   Max.   :84.49  
##    residuals       abs_residuals        label                ids       
##  Min.   :-6.0612   Min.   :0.06061   Length:72          Min.   : 1.00  
##  1st Qu.:-1.2126   1st Qu.:0.72930   Class :character   1st Qu.:18.75  
##  Median : 0.2159   Median :1.29306   Mode  :character   Median :36.50  
##  Mean   : 0.1293   Mean   :1.77153                      Mean   :36.50  
##  3rd Qu.: 1.5601   3rd Qu.:2.67075                      3rd Qu.:54.25  
##  Max.   : 7.8218   Max.   :7.82176                      Max.   :72.00
plot(NN_DALEX_Diagnostics,
     variable = "y",
     yvariable = "y_hat") +
  geom_point(size=3) +
  scale_x_continuous("Observed LIFEXP") +
  scale_y_continuous("Predicted LIFEXP") +
  geom_abline(slope = 1) +
  ggtitle("NN: Observed and Predicted LIFEXP")

##################################
# Formulating the DALEX object
# for the Best PLS model
# as applied to the model test data
##################################
PLS_DALEX <- DALEX::explain(PLS_Tune,
                            data = MT.Model.Predictors,
                            y = MT$LIFEXP,
                            verbose = FALSE,
                            label = "PLS")

(PLS_DALEX_Performance <- model_performance(PLS_DALEX))
## Measures for:  regression
## mse        : 7.220162 
## rmse       : 2.687036 
## r2         : 0.8644985 
## mad        : 1.875765
## 
## Residuals:
##         0%        10%        20%        30%        40%        50%        60% 
## -6.7868694 -2.5089071 -1.8150301 -1.1801931 -0.4920039  0.1375484  0.7844976 
##        70%        80%        90%       100% 
##  2.0494752  2.6262401  3.8453344  6.0828174
(PLS_DALEX_Diagnostics <- model_diagnostics(PLS_DALEX))
##     GENDER             CONTIN       INFMOR           PERCAP       
##  Male  :43   Africa       :17   Min.   :0.6419   Min.   :-1.4775  
##  Female:29   Asia         :21   1st Qu.:1.7090   1st Qu.: 0.7926  
##              Europe       :16   Median :2.6602   Median : 1.7266  
##              North America:11   Mean   :2.5559   Mean   : 1.7439  
##              Oceania      : 2   3rd Qu.:3.4135   3rd Qu.: 2.8314  
##              South America: 5   Max.   :4.3858   Max.   : 4.4466  
##      CLTECH           NCOMOR            y             y_hat      
##  Min.   :  0.20   Min.   :2.511   Min.   :51.20   Min.   :57.14  
##  1st Qu.: 49.02   1st Qu.:3.887   1st Qu.:67.42   1st Qu.:67.18  
##  Median : 90.20   Median :4.742   Median :73.51   Median :72.99  
##  Mean   : 72.08   Mean   :4.752   Mean   :72.61   Mean   :72.33  
##  3rd Qu.:100.00   3rd Qu.:5.631   3rd Qu.:78.47   3rd Qu.:77.39  
##  Max.   :100.00   Max.   :7.406   Max.   :86.20   Max.   :84.75  
##    residuals       abs_residuals        label                ids       
##  Min.   :-6.7869   Min.   :0.04887   Length:72          Min.   : 1.00  
##  1st Qu.:-1.4065   1st Qu.:0.82974   Class :character   1st Qu.:18.75  
##  Median : 0.1375   Median :1.87576   Mode  :character   Median :36.50  
##  Mean   : 0.2816   Mean   :2.15539                      Mean   :36.50  
##  3rd Qu.: 2.4014   3rd Qu.:3.14434                      3rd Qu.:54.25  
##  Max.   : 6.0828   Max.   :6.78687                      Max.   :72.00
plot(PLS_DALEX_Diagnostics,
     variable = "y",
     yvariable = "y_hat") +
  geom_point(size=3) +
  scale_x_continuous("Observed LIFEXP") +
  scale_y_continuous("Predicted LIFEXP") +
  geom_abline(slope = 1) +
  ggtitle("PLS: Observed and Predicted LIFEXP")

##################################
# Formulating the DALEX object
# for the Best CUBIST model
# as applied to the model test data
##################################
CUBIST_DALEX <- DALEX::explain(CUBIST_Tune,
                            data = MT.Model.Predictors,
                            y = MT$LIFEXP,
                            verbose = FALSE,
                            label = "CUBIST")

(CUBIST_DALEX_Performance <- model_performance(CUBIST_DALEX))
## Measures for:  regression
## mse        : 4.955851 
## rmse       : 2.226174 
## r2         : 0.9069931 
## mad        : 1.555514
## 
## Residuals:
##          0%         10%         20%         30%         40%         50% 
## -6.27483923 -2.34803998 -1.75093724 -1.08073022 -0.25740164  0.08468332 
##         60%         70%         80%         90%        100% 
##  0.74473043  1.16073038  1.70618640  2.84412369  5.15525385
(CUBIST_DALEX_Diagnostics <- model_diagnostics(CUBIST_DALEX))
##     GENDER             CONTIN       INFMOR           PERCAP       
##  Male  :43   Africa       :17   Min.   :0.6419   Min.   :-1.4775  
##  Female:29   Asia         :21   1st Qu.:1.7090   1st Qu.: 0.7926  
##              Europe       :16   Median :2.6602   Median : 1.7266  
##              North America:11   Mean   :2.5559   Mean   : 1.7439  
##              Oceania      : 2   3rd Qu.:3.4135   3rd Qu.: 2.8314  
##              South America: 5   Max.   :4.3858   Max.   : 4.4466  
##      CLTECH           NCOMOR            y             y_hat      
##  Min.   :  0.20   Min.   :2.511   Min.   :51.20   Min.   :55.56  
##  1st Qu.: 49.02   1st Qu.:3.887   1st Qu.:67.42   1st Qu.:67.93  
##  Median : 90.20   Median :4.742   Median :73.51   Median :72.67  
##  Mean   : 72.08   Mean   :4.752   Mean   :72.61   Mean   :72.49  
##  3rd Qu.:100.00   3rd Qu.:5.631   3rd Qu.:78.47   3rd Qu.:77.88  
##  Max.   :100.00   Max.   :7.406   Max.   :86.20   Max.   :84.71  
##    residuals        abs_residuals        label                ids       
##  Min.   :-6.27484   Min.   :0.01256   Length:72          Min.   : 1.00  
##  1st Qu.:-1.54890   1st Qu.:0.71120   Class :character   1st Qu.:18.75  
##  Median : 0.08468   Median :1.55551   Mode  :character   Median :36.50  
##  Mean   : 0.11581   Mean   :1.73586                      Mean   :36.50  
##  3rd Qu.: 1.40404   3rd Qu.:2.38064                      3rd Qu.:54.25  
##  Max.   : 5.15525   Max.   :6.27484                      Max.   :72.00
plot(CUBIST_DALEX_Diagnostics,
     variable = "y",
     yvariable = "y_hat") +
  geom_point(size=3) +
  scale_x_continuous("Observed LIFEXP") +
  scale_y_continuous("Predicted LIFEXP") +
  geom_abline(slope = 1) +
  ggtitle("CUBIST: Observed and Predicted LIFEXP")

1.3.8 Model Selection


Code Chunk | Output
##################################
# Consolidating the performance
# on the model test data
##################################
plot(GBM_DALEX_Performance,
     RF_DALEX_Performance,
     NN_DALEX_Performance,
     PLS_DALEX_Performance,
     CUBIST_DALEX_Performance)

plot(GBM_DALEX_Performance,
     RF_DALEX_Performance,
     NN_DALEX_Performance,
     PLS_DALEX_Performance,
     CUBIST_DALEX_Performance,
     geom = "boxplot")

plot(GBM_DALEX_Performance,
     RF_DALEX_Performance,
     NN_DALEX_Performance,
     PLS_DALEX_Performance,
     CUBIST_DALEX_Performance,
     geom = "histogram")

##################################
# Consolidating the variable importance
# on the model test data
##################################
GBM_DALEX_VariableImportance    <- model_parts(GBM_DALEX,
                                               loss_function = loss_root_mean_square,
                                               B = 200,
                                               N = NULL)
RF_DALEX_VariableImportance     <- model_parts(RF_DALEX,
                                               loss_function = loss_root_mean_square,
                                               B = 200,
                                               N = NULL)
NN_DALEX_VariableImportance     <- model_parts(NN_DALEX,
                                               loss_function = loss_root_mean_square,
                                               B = 200,
                                               N = NULL)
PLS_DALEX_VariableImportance    <- model_parts(PLS_DALEX,
                                               loss_function = loss_root_mean_square,
                                               B = 200,
                                               N = NULL)
CUBIST_DALEX_VariableImportance <- model_parts(CUBIST_DALEX,
                                               loss_function = loss_root_mean_square,
                                               B = 200,
                                               N = NULL)

plot(GBM_DALEX_VariableImportance,
     RF_DALEX_VariableImportance,
     NN_DALEX_VariableImportance,
     PLS_DALEX_VariableImportance,
     CUBIST_DALEX_VariableImportance)

1.3.9 Model Post-Hoc Analysis


1.3.9.1 Dataset Level Exploration : Variable Importance (DLE_VARIMP)


Code Chunk | Output
##################################
# Summarizing the variable importance
# for the final model - GBM
##################################
GBM_DALEX_VariableImportance
##       variable mean_dropout_loss label
## 1 _full_model_          2.208545   GBM
## 2       PERCAP          2.373317   GBM
## 3       CLTECH          2.479101   GBM
## 4       GENDER          2.625180   GBM
## 5       CONTIN          2.633023   GBM
## 6       NCOMOR          3.995838   GBM
## 7       INFMOR          7.080997   GBM
## 8   _baseline_         10.054004   GBM
plot(GBM_DALEX_VariableImportance)


1.3.9.2 Dataset Level Exploration : Partial Dependence Plots (DLE_PDP)


Code Chunk | Output
##################################
# Formulating the partial dependence plots
# for the final model - GBM
# using the numeric variables
##################################
GBM_DALEX_PartialDependencePlot_INFMOR <- model_profile(GBM_DALEX,
                                                        variables = "INFMOR")
GBM_DALEX_PartialDependencePlot_NCOMOR <- model_profile(GBM_DALEX,
                                                        variables = "NCOMOR")
GBM_DALEX_PartialDependencePlot_CLTECH <- model_profile(GBM_DALEX,
                                                        variables = "CLTECH")
GBM_DALEX_PartialDependencePlot_PERCAP <- model_profile(GBM_DALEX,
                                                        variables = "PERCAP")

(GBM_DALEX_PDP_INFMOR <- plot(GBM_DALEX_PartialDependencePlot_INFMOR,
                              geom = "profiles"))

(GBM_DALEX_PDP_NCOMOR <- plot(GBM_DALEX_PartialDependencePlot_NCOMOR,
                              geom = "profiles"))

(GBM_DALEX_PDP_CLTECH <- plot(GBM_DALEX_PartialDependencePlot_CLTECH,
                              geom = "profiles"))

(GBM_DALEX_PDP_PERCAP <- plot(GBM_DALEX_PartialDependencePlot_PERCAP,
                              geom = "profiles"))

##################################
# Formulating the grouped partial dependence plots
# for the final model - GBM
# using the numeric variables
# stratified by GENDER
##################################
GBM_DALEX_GroupedPartialDependencePlot_INFMOR <- model_profile(GBM_DALEX,
                                                               variables = "INFMOR",
                                                               groups = "GENDER")
GBM_DALEX_GroupedPartialDependencePlot_NCOMOR <- model_profile(GBM_DALEX,
                                                               variables = "NCOMOR",
                                                               groups = "GENDER")
GBM_DALEX_GroupedPartialDependencePlot_CLTECH <- model_profile(GBM_DALEX,
                                                               variables = "CLTECH",
                                                               groups = "GENDER")
GBM_DALEX_GroupedPartialDependencePlot_PERCAP <- model_profile(GBM_DALEX,
                                                               variables = "PERCAP",
                                                               groups = "GENDER")

(GBM_DALEX_GPDP_INFMOR <- plot(GBM_DALEX_GroupedPartialDependencePlot_INFMOR,
                              geom = "profiles"))

(GBM_DALEX_GPDP_NCOMOR <- plot(GBM_DALEX_GroupedPartialDependencePlot_NCOMOR,
                              geom = "profiles"))

(GBM_DALEX_GPDP_CLTECH <- plot(GBM_DALEX_GroupedPartialDependencePlot_CLTECH,
                              geom = "profiles"))

(GBM_DALEX_GPDP_PERCAP <- plot(GBM_DALEX_GroupedPartialDependencePlot_PERCAP,
                              geom = "profiles"))

##################################
# Formulating the grouped partial dependence plots
# for the final model - GBM
# using the numeric variables
# stratified by CONTIN
##################################
GBM_DALEX_GroupedPartialDependencePlot_INFMOR <- model_profile(GBM_DALEX,
                                                               variables = "INFMOR",
                                                               groups = "CONTIN")
GBM_DALEX_GroupedPartialDependencePlot_NCOMOR <- model_profile(GBM_DALEX,
                                                               variables = "NCOMOR",
                                                               groups = "CONTIN")
GBM_DALEX_GroupedPartialDependencePlot_CLTECH <- model_profile(GBM_DALEX,
                                                               variables = "CLTECH",
                                                               groups = "CONTIN")
GBM_DALEX_GroupedPartialDependencePlot_PERCAP <- model_profile(GBM_DALEX,
                                                               variables = "PERCAP",
                                                               groups = "CONTIN")

(GBM_DALEX_GPDP_INFMOR <- plot(GBM_DALEX_GroupedPartialDependencePlot_INFMOR,
                              geom = "profiles"))

(GBM_DALEX_GPDP_NCOMOR <- plot(GBM_DALEX_GroupedPartialDependencePlot_NCOMOR,
                              geom = "profiles"))

(GBM_DALEX_GPDP_CLTECH <- plot(GBM_DALEX_GroupedPartialDependencePlot_CLTECH,
                              geom = "profiles"))

(GBM_DALEX_GPDP_PERCAP <- plot(GBM_DALEX_GroupedPartialDependencePlot_PERCAP,
                              geom = "profiles"))

##################################
# Formulating the partial dependence plots
# for the final model - GBM
# using the factor variables
##################################
GBM_DALEX_PartialDependencePlot_GENDER <- model_profile(GBM_DALEX,
                                                        variable_type = 'categorical',
                                                        variables = "GENDER")
GBM_DALEX_PartialDependencePlot_CONTIN <- model_profile(GBM_DALEX,
                                                        variable_type = 'categorical',
                                                        variables = "CONTIN")

(GBM_DALEX_PDP_GENDER <- plot(GBM_DALEX_PartialDependencePlot_GENDER,
                               geom = "profiles"))

(GBM_DALEX_PDP_CONTIN <- plot(GBM_DALEX_PartialDependencePlot_CONTIN,
                               geom = "profiles"))


1.3.9.3 Instance Level Exploration : Breakdown Plots (ILE_BP)


Code Chunk | Output
##################################
# Formulating the sampled instances
# for illustration
##################################
(Instance_1_Philippines_Female  <- PME[PME$COUNTRY=="Philippines" & PME$GENDER=="Female",
                                   c("GENDER","CONTIN","INFMOR","PERCAP","CLTECH","NCOMOR","LIFEXP")])
##     GENDER CONTIN   INFMOR   PERCAP CLTECH   NCOMOR LIFEXP
## 141 Female   Asia 2.944439 1.248566   47.4 4.704261 75.505
(Instance_2_Philippines_Male    <- PME[PME$COUNTRY=="Philippines" & PME$GENDER=="Male",
                                   c("GENDER","CONTIN","INFMOR","PERCAP","CLTECH","NCOMOR","LIFEXP")])
##     GENDER CONTIN   INFMOR   PERCAP CLTECH   NCOMOR LIFEXP
## 338   Male   Asia 3.173878 1.248566   47.4 5.950788 67.263
##################################
# Obtaining the breakdown plots
# for the individual instances
##################################
(Instance_1_GBM_BDP <- DALEX::predict_parts(explainer = GBM_DALEX,
                                           new_observation = Instance_1_Philippines_Female[,c(1:6)],
                                           type = "break_down"))
##                      contribution
## GBM: intercept             72.334
## GBM: GENDER = Female        1.285
## GBM: CONTIN = Asia          0.525
## GBM: INFMOR = 2.944         0.695
## GBM: NCOMOR = 4.704        -0.161
## GBM: PERCAP = 1.249        -0.111
## GBM: CLTECH = 47.4          0.014
## GBM: prediction            74.581
plot(Instance_1_GBM_BDP)

(Instance_2_GBM_BDP <- DALEX::predict_parts(explainer = GBM_DALEX,
                                           new_observation = Instance_2_Philippines_Male[,c(1:6)],
                                           type = "break_down"))
##                     contribution
## GBM: intercept            72.334
## GBM: NCOMOR = 5.951       -3.063
## GBM: INFMOR = 3.174       -0.819
## GBM: GENDER = Male        -0.926
## GBM: CONTIN = Asia         0.813
## GBM: PERCAP = 1.249        0.256
## GBM: CLTECH = 47.4        -0.142
## GBM: prediction           68.452
plot(Instance_2_GBM_BDP)


1.3.9.4 Instance Level Exploration : Shapley Additive Explanations (ILE_SHAP)


Code Chunk | Output
##################################
# Obtaining the shapley additive explanations
# for the individual instances
##################################
(Instance_1_GBM_SHAP <- DALEX::predict_parts(explainer = GBM_DALEX,
                                           new_observation = Instance_1_Philippines_Female[,c(1:6)],
                                           type = "shap",
                                           B = 25))
##                             min          q1     median        mean          q3
## GBM: CLTECH = 47.4   -0.1096761  0.01141644  0.0425315  0.04627025  0.11125913
## GBM: CONTIN = Asia    0.1997490  0.27790908  0.4226572  0.42047277  0.55714718
## GBM: GENDER = Female  1.0250692  1.17867184  1.2296506  1.22132808  1.28532575
## GBM: INFMOR = 2.944   0.5616631  0.60727978  1.2181237  1.12691237  1.49359879
## GBM: NCOMOR = 4.704  -0.9672737 -0.69107878 -0.5245096 -0.47182445 -0.17498794
## GBM: PERCAP = 1.249  -0.3942109 -0.20796143 -0.1052200 -0.09626333 -0.08089423
##                            max
## GBM: CLTECH = 47.4   0.1437923
## GBM: CONTIN = Asia   0.6872704
## GBM: GENDER = Female 1.3923546
## GBM: INFMOR = 2.944  1.6130390
## GBM: NCOMOR = 4.704  0.0998502
## GBM: PERCAP = 1.249  0.1876230
plot(Instance_1_GBM_SHAP)

(Instance_2_GBM_SHAP <- DALEX::predict_parts(explainer = GBM_DALEX,
                                           new_observation = Instance_2_Philippines_Male[,c(1:6)],
                                           type = "shap",
                                           B = 25))
##                            min          q1      median        mean          q3
## GBM: CLTECH = 47.4  -0.1833863 -0.13902978 -0.10967607 -0.08443553 -0.03754566
## GBM: CONTIN = Asia   0.4248417  0.61800333  0.62294753  0.63130944  0.66081998
## GBM: GENDER = Male  -1.0374690 -0.89214274 -0.80993627 -0.79832321 -0.71019714
## GBM: INFMOR = 3.174 -2.0087792 -1.95450059 -1.74699387 -1.44666001 -0.81061732
## GBM: NCOMOR = 5.951 -3.1802712 -2.89954326 -1.90426390 -2.23039094 -1.80864334
## GBM: PERCAP = 1.249 -0.2740559 -0.08089423 -0.01069142  0.04676586  0.24048722
##                             max
## GBM: CLTECH = 47.4   0.06412474
## GBM: CONTIN = Asia   0.90402611
## GBM: GENDER = Male  -0.61636661
## GBM: INFMOR = 3.174 -0.48666306
## GBM: NCOMOR = 5.951 -1.46373246
## GBM: PERCAP = 1.249  0.26382681
plot(Instance_2_GBM_SHAP)


1.3.9.5 Instance Level Exploration : Ceteris Paribus Profiles (ILE_CPP)


Code Chunk | Output
##################################
# Obtaining the ceteris paribus profiles
# for the individual instances
##################################
(Instance_1_GBM_CPP <- DALEX::predict_profile(explainer = GBM_DALEX,
                                           new_observation = Instance_1_Philippines_Female[,c(1:6)]))
## Top profiles    : 
##         GENDER        CONTIN   INFMOR   PERCAP CLTECH   NCOMOR   _yhat_ _vname_
## 141       Male          Asia 2.944439 1.248566   47.4 4.704261 72.52199  GENDER
## 141.1   Female          Asia 2.944439 1.248566   47.4 4.704261 74.58094  GENDER
## 1411    Female        Africa 2.944439 1.248566   47.4 4.704261 73.53995  CONTIN
## 141.110 Female          Asia 2.944439 1.248566   47.4 4.704261 74.58094  CONTIN
## 141.2   Female        Europe 2.944439 1.248566   47.4 4.704261 74.92891  CONTIN
## 141.3   Female North America 2.944439 1.248566   47.4 4.704261 74.52190  CONTIN
##         _ids_ _label_
## 141       141     GBM
## 141.1     141     GBM
## 1411      141     GBM
## 141.110   141     GBM
## 141.2     141     GBM
## 141.3     141     GBM
## 
## 
## Top observations:
##     GENDER CONTIN   INFMOR   PERCAP CLTECH   NCOMOR   _yhat_ _label_ _ids_
## 141 Female   Asia 2.944439 1.248566   47.4 4.704261 74.58094     GBM     1
plot(Instance_1_GBM_CPP,
     variables = c("INFMOR","PERCAP","CLTECH","NCOMOR")) +
  ggtitle("Ceteris-paribus profile", "") + 
  ylim(55, 80)

plot(Instance_1_GBM_CPP,
     variables = c("GENDER","CONTIN"), 
     variable_type = "categorical", 
     categorical_type = "bars") +
  ggtitle("Ceteris-paribus profile", "")

(Instance_2_GBM_CPP <- DALEX::predict_profile(explainer = GBM_DALEX,
                                           new_observation = Instance_2_Philippines_Male[,c(1:6)]))
## Top profiles    : 
##         GENDER        CONTIN   INFMOR   PERCAP CLTECH   NCOMOR   _yhat_ _vname_
## 338       Male          Asia 3.173878 1.248566   47.4 5.950788 68.45231  GENDER
## 338.1   Female          Asia 3.173878 1.248566   47.4 5.950788 70.66729  GENDER
## 3381      Male        Africa 3.173878 1.248566   47.4 5.950788 66.21920  CONTIN
## 338.110   Male          Asia 3.173878 1.248566   47.4 5.950788 68.45231  CONTIN
## 338.2     Male        Europe 3.173878 1.248566   47.4 5.950788 68.11751  CONTIN
## 338.3     Male North America 3.173878 1.248566   47.4 5.950788 68.25643  CONTIN
##         _ids_ _label_
## 338       338     GBM
## 338.1     338     GBM
## 3381      338     GBM
## 338.110   338     GBM
## 338.2     338     GBM
## 338.3     338     GBM
## 
## 
## Top observations:
##     GENDER CONTIN   INFMOR   PERCAP CLTECH   NCOMOR   _yhat_ _label_ _ids_
## 338   Male   Asia 3.173878 1.248566   47.4 5.950788 68.45231     GBM     1
plot(Instance_2_GBM_CPP,
     variables = c("INFMOR","PERCAP","CLTECH","NCOMOR")) +
  ggtitle("Ceteris-paribus profile", "") + 
  ylim(55, 80)

plot(Instance_2_GBM_CPP,
     variables = c("GENDER","CONTIN"), 
     variable_type = "categorical", 
     categorical_type = "bars") +
  ggtitle("Ceteris-paribus profile", "")


1.3.9.6 Instance Level Exploration : Local Fidelity Plots (ILE_LFP)


Code Chunk | Output
Instance_1_GBM_LFP <- predict_diagnostics(explainer = GBM_DALEX,
                                         new_observation = Instance_1_Philippines_Female[,c(1:6)],
                                         neighbours = 50)
plot(Instance_1_GBM_LFP)

Instance_2_GBM_LFP <- predict_diagnostics(explainer = GBM_DALEX,
                                         new_observation = Instance_2_Philippines_Male[,c(1:6)],
                                         neighbours = 50)
plot(Instance_2_GBM_LFP)


1.3.9.7 Instance Level Exploration : Local Stability Plots (ILE_LSP)


Code Chunk | Output
Instance_1_GBM_LFP <- predict_diagnostics(explainer = GBM_DALEX,
                                          new_observation = Instance_1_Philippines_Female[,c(1:6)],
                                          neighbours = 5,
                                          variables = c("INFMOR","NCOMOR","CLTECH","PERCAP"))
plot(Instance_1_GBM_LFP)

Instance_1_GBM_LFP <- predict_diagnostics(explainer = GBM_DALEX,
                                          new_observation = Instance_1_Philippines_Female[,c(1:6)],
                                          neighbours = 5,
                                          variables = c("GENDER","CONTIN"))
plot(Instance_1_GBM_LFP)

Instance_2_GBM_LFP <- predict_diagnostics(explainer = GBM_DALEX,
                                          new_observation = Instance_2_Philippines_Male[,c(1:6)],
                                          neighbours = 5,
                                          variables = c("INFMOR","NCOMOR","CLTECH","PERCAP"))
plot(Instance_2_GBM_LFP)

Instance_2_GBM_LFP <- predict_diagnostics(explainer = GBM_DALEX,
                                          new_observation = Instance_2_Philippines_Male[,c(1:6)],
                                          neighbours = 5,
                                          variables = c("GENDER","CONTIN"))
plot(Instance_2_GBM_LFP)

1.4 Summary

2. References


[Book] Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models With examples in R and Python by Przemyslaw Biecek and Tomasz Burzykowski
[Book] Explainable Machine Learning: A Guide for Making Black Box Models Explainable by Christoph Molnar
[Book] Explainable AI: Interpreting, Explaining and Visualizing Deep Learning by Wojciech Samek, Gregoire Montavon, Andrea Vedaldi, Lars Kai Hansen and Klaus-Robert Muller
[Book] Applied Predictive Modeling by Max Kuhn and Kjell Johnson
[Book] The Elements of Statistical Learning by Trevor Hastie , Robert Tibshirani and Jerome Friedman
[Book] Pattern Recognition and Neural Networks by Brian Ripley
[R Package] DALEX by Przemyslaw Biecek, Szymon Maksymiuk and Hubert Baniecki
[R Package] iml by Christoph Molnar
[R Package] ALEPlot by Dan Apley
[R Package] randomForest by Leo Breiman, Adele Cutler, Andy Liaw and Matthew Wiener
[R Package] auditor by Alicja Gosiewska, Przemyslaw Biecek, Hubert Baniecki and Tomasz Mikołajczyk
[R Package] fastshap by Brandon Greenwell
[R Package] rms by Frank Harrell
[R Package] EIX by Szymon Maksymiuk, Ewelina Karbowiak and Przemyslaw Biecek
[R Package] parsnip by Max Kuhn and Davis Vaughan
[R Package] h2o by Tomas Fryda, Erin LeDell, Navdeep Gill, Spencer Aiello, Anqi Fu, Arno Candel, Cliff Click, Tom Kraljevic, Tomas Nykodym, Patrick Aboyoun, Michal Kurka, Michal Malohlava, Sebastien Poirier and Wendy Wong
[R Package] tidymodels by Max Kuhn and Hadley Wickham
[R Package] e1071 by David Meyer, Evgenia Dimitriadou, Kurt Hornik, Andreas Weingessel and Friedrich Leisch
[R Package] lime by Emil Hvitfeldt, Thomas Lin Pedersen and Michael Benesty
[R Package] ExplainPrediction by Marko Robnik-Sikonja
[R Package] localModel by Przemyslaw Biecek and Mateusz Staniak
[R Package] skimr by Elin Waring
[R Package] corrplot by Taiyun Wei
[R Package] lares by Bernardo Lares
[R Package] minerva by Michele Filosi
[R Package] CORElearn by Marko Robnik-Sikonja and Petr Savicky
[R Package] caret by Max Kuhn
[R Package] gbm by Brandon Greenwell, Bradley Boehmke, Jay Cunningham and GBM Developers
[R Package] randomForest by Andy Liaw
[R Package] nnet by Brian Ripley
[R Package] pls by Kristian Hovde Liland
[R Package] Cubist by Max Kuhn
[R Package] patchwork by Thomas Lin Pedersen
[Article] Interpretation Methods for Black-Box Machine Learning Models in Insurance Rating-Type Applications by Gabe Taylor, Sunish Menon, Huimin Ru, Ray Wright, Xin Hunt and Ralph Abbey
[Article] 4 Model-Agnostic Interpretability Techniques for Complex Models by Funda Gunes
[Article] How Can We Provide Post-Hoc Explanations for Black-Box AI Models? by Joy Lin
[Article] Correlation in R: Pearson and Spearman Correlation Matrix by Daniel Johnson
[Article] Correlation (Pearson, Kendall, Spearman) by Statistics Solutions Team
[Article] A Comparison of the Pearson and Spearman Correlation Methods by Minitab Support Team
[Article] How to Perform Lowess Smoothing in R (Step-by-Step) by Statology Team
[Article] Maximal Information Coefficient by R Bloggers Team
[Article] Methods for Forecasts of Continuous Variables by WWRP/WGNE Joint Working Group on Forecast Verification Research Team
[Article] Generalized Boosting Model by BCCVL Team
[Article] An Introduction to Partial Least Squares by Statology Team
[Article] Random Forest by BCCVL Team
[Article] Artificial Neural Network by BCCVL Team
[Article] Cubist Regression Models by Max Kuhn
[Publication] Robust Locally Weighted Regression and Smoothing Scatterplots by William Cleveland (Journal of the American Statistical Association)
[Publication] Mathematical Contributions to the Theory of Evolution: Regression, Heredity and Panmixia by Karl Pearson (Royal Society)
[Publication] The Proof and Measurement of Association between Two Things by Charles Spearman (The American Journal of Psychology)
[Publication] Detecting Novel Associations in Large Data Sets by David Reshef, Yakir Reshef, Hilary Finucane, Sharon Grossman, Gilean Mcvean, Peter Turnbaugh, Eric Lander, Michael Mitzenmacher and Pardis Sabeti (Science)
[Publication] Stochastic Gradient Boosting by Jerome Friedman (Computational Statistics and Data Analysis)
[Publication] Random Forest by Leo Breiman (Machine Learning)
[Publication] The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses by Svante Wold, Axel Ruhe, Herman Wold, and William Dunn (Society for Industrial and Applied Mathematics)
[Publication] Learning With Continuous Classes by Ross Quinlan (Proceedings of the 5th Australian Joint Conference On Artificial Intelligence)
[Publication] A Survey of Methods for Explaining Black Box Models by Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti and Dino Pedreschi (ACM Computing Surveys)
[Publication] iml: An R package for Interpretable Machine Learning by Christoph Molnar (Journal of Open Source Software)
[Publication] All Models Are Wrong, but Many Are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously by Aaron Fisher, Cynthia Rudin and Francesca Dominici (Journal of Machine Learning Research)
[Publication] Greedy Function Approximation: A Gradient Boosting Machine by Jerome Friedman (Annals of Statistics)
[Publication] Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation by Alex Goldstein, Adam Kapelner, Justin Bleich and Emil Pitkin (Journal of Computational and Graphical Statistics)
[Publication] An Efficient Explanation of Individual Classifications Using Game Theory by Erik Strumbelj and Igor Kononenko (Journal of Machine Learning Research)
[Publication] Explaining Classifications For Individual Instances by Marco Robnik-Sikonja and Igor Kononenko (IEEE Transactions on Knowledge and Data Engineering)